Spaces:
Running on Zero
Running on Zero
| # app.py - Encapsulated version of the Colab notebook for deployment as a web app (e.g., on Hugging Face Spaces) | |
| # This refactors the original 1501 lines into a modular structure: global model loading, a main processing function, | |
| # and a Gradio interface. Based on the notebook's structure, which includes text preprocessing, sign tagging with | |
| # Hugging Face models, span tagging, and recursive simplification. Full original functions are assumed to be | |
| # integrated here (placeholders for complex ones like SSCCVsimplify). Adjust as needed if you have the exact defs. | |
| ''' | |
| cd d:\TextSimplificationDemo | |
| # Delete old venv (if needed) | |
| Remove-Item -Recurse -Force .\venv | |
| # Create venv with Python 3.11 | |
| py -3.11 -m venv venv | |
| # Activate it | |
| .\venv\Scripts\Activate.ps1 | |
| # Upgrade pip | |
| python -m pip install --upgrade pip | |
| # Install packages | |
| pip install -r requirements.txt | |
| # Install spacy-alignments safely | |
| pip install spacy-alignments --only-binary :all: | |
| # Download spaCy model | |
| python -m spacy download en_core_web_sm | |
| # Run the app | |
| python app.py | |
| # Interface at http://127.0.0.1:7860/ | |
| EXAMPLE SENTENCE: | |
| John is one test, Mary, who lives in London, loves exams, and Bill eats bread. | |
| John went to Wolverhampton, Bill set out for Coventry, and Mary returned from Birmingham. | |
| The Environment Agency said record winter rainfall had helped to replenish reservoirs and groundwater, but reservoir storage has now fallen to 7.4% below the average for this time of year. | |
| The Environment Agency said record winter rainfall had helped to replenish reservoirs and groundwater, but reservoir storage has now fallen to 7.4 below the average for this time of year. | |
| Reservoir storage of 8.2 has been recorded and Mary went home. | |
| The demo loops infinitely when more than 1 sentence is included on the line. | |
| John, who lives in Wolverhampton, which is a town in the West Midlands, went home. | |
| ''' | |
| import spaces | |
| import re | |
| import sys | |
| import torch | |
| from transformers import AutoModelForTokenClassification, AutoTokenizer | |
| import spacy | |
| import xml.etree.ElementTree as ET | |
| import spacy_alignments as tokenizations | |
| import xml.dom.minidom | |
| import pandas as pd | |
| import gradio as gr | |
| import os | |
| from collections import deque # add this at the top of app.py if not already there | |
| from collections import defaultdict | |
| PRONOUNS = { | |
| "he", "she", "it", "they", "him", "her", "them", | |
| "his", "hers", "its", "their", "theirs", | |
| } | |
| seen = set() | |
| import io | |
| hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print("Device:", device) | |
| # Fix for Windows Unicode printing issues | |
| if sys.platform == "win32": | |
| sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace') | |
| # Global configurations | |
| test_on_unrestricted_text = True # Set to False for test sentences | |
| # Load spaCy model | |
| nlp = spacy.load("en_core_web_sm") # Assuming this is used; load once | |
| # Load Hugging Face models and tokenizers (done globally to avoid reloading) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print("Device:", device) | |
| sign_tagging_model_name = "RJ3vans/SignTagger" | |
| SignTaggingTokenizer = AutoTokenizer.from_pretrained(sign_tagging_model_name, token=hf_token) | |
| SignTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| sign_tagging_model_name, token=hf_token | |
| ).to(device) | |
| SignTaggingModel.eval() | |
| ssccv_span_tagging_model_name = "RJ3vans/SSCCVspanTagger" | |
| SSCCVspanTaggingTokenizer = AutoTokenizer.from_pretrained(ssccv_span_tagging_model_name, token=hf_token) | |
| SSCCVspanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| ssccv_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| SSCCVspanTaggingModel.eval() | |
| ssmn_span_tagging_model_name = "RJ3vans/SSMNspanTagger" | |
| SSMNspanTaggingTokenizer = AutoTokenizer.from_pretrained(ssmn_span_tagging_model_name, token=hf_token) | |
| SSMNspanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| ssmn_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| SSMNspanTaggingModel.eval() | |
| ccv_span_tagging_model_name = "RJ3vans/CCVspanTagger" | |
| CCVspanTaggingTokenizer = AutoTokenizer.from_pretrained(ccv_span_tagging_model_name, token=hf_token) | |
| CCVspanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| ccv_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| CCVspanTaggingModel.eval() | |
| cmv1_span_tagging_model_name = "RJ3vans/CMV1spanTagger" | |
| CMV1spanTaggingTokenizer = AutoTokenizer.from_pretrained(cmv1_span_tagging_model_name, token=hf_token) | |
| CMV1spanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| cmv1_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| CMV1spanTaggingModel.eval() | |
| cmn1_span_tagging_model_name = "RJ3vans/CMN1spanTagger" | |
| CMN1spanTaggingTokenizer = AutoTokenizer.from_pretrained(cmn1_span_tagging_model_name, token=hf_token) | |
| CMN1spanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| cmn1_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| CMN1spanTaggingModel.eval() | |
| cln_span_tagging_model_name = "RJ3vans/CLNspanTagger" | |
| CLNspanTaggingTokenizer = AutoTokenizer.from_pretrained(cln_span_tagging_model_name, token=hf_token) | |
| CLNspanTaggingModel = AutoModelForTokenClassification.from_pretrained( | |
| cln_span_tagging_model_name, token=hf_token | |
| ).to(device) | |
| CLNspanTaggingModel.eval() | |
| # Define label lists (extracted from notebook summary; expand with full 70+ if needed) | |
| sign_label_list = [ | |
| "M:N_CCV", | |
| "M:N_CIN", | |
| "M:N_CLA", | |
| "M:N_CLAdv", | |
| "M:N_CLN", | |
| "M:N_CLP", | |
| "M:N_CLQ", | |
| "M:N_CLV", | |
| "M:N_CMA1", | |
| "M:N_CMAdv", | |
| "M:N_CMN1", | |
| "M:N_CMN2", | |
| "M:N_CMN3", | |
| "M:N_CMN4", | |
| "M:N_CMP", | |
| "M:N_CMP2", | |
| "M:N_CMV1", | |
| "M:N_CMV2", | |
| "M:N_CMV3", | |
| "M:N_COMBINATORY", | |
| "M:N_CPA", | |
| "M:N_ESAdvP", | |
| "M:N_ESCCV", | |
| "M:N_ESCM", | |
| "M:N_ESMA", | |
| "M:N_ESMAdvP", | |
| "M:N_ESMI", | |
| "M:N_ESMN", | |
| "M:N_ESMP", | |
| "M:N_ESMV", | |
| "M:N_HELP", | |
| "M:N_SPECIAL", | |
| "M:N_SSCCV", | |
| "M:N_SSCM", | |
| "M:N_SSMA", | |
| "M:N_SSMAdvP", | |
| "M:N_SSMI", | |
| "M:N_SSMN", | |
| "M:N_SSMP", | |
| "M:N_SSMV", | |
| "M:N_STQ", | |
| "M:N_V", | |
| "M:N_nan", | |
| "M:Y_CCV", | |
| "M:Y_CIN", | |
| "M:Y_CLA", | |
| "M:Y_CLAdv", | |
| "M:Y_CLN", | |
| "M:Y_CLP", | |
| "M:Y_CLQ", | |
| "M:Y_CLV", | |
| "M:Y_CMA1", | |
| "M:Y_CMAdv", | |
| "M:Y_CMN1", | |
| "M:Y_CMN2", | |
| "M:Y_CMN4", | |
| "M:Y_CMP", | |
| "M:Y_CMP2", | |
| "M:Y_CMV1", | |
| "M:Y_CMV2", | |
| "M:Y_CMV3", | |
| "M:Y_COMBINATORY", | |
| "M:Y_CPA", | |
| "M:Y_ESAdvP", | |
| "M:Y_ESCCV", | |
| "M:Y_ESCM", | |
| "M:Y_ESMA", | |
| "M:Y_ESMAdvP", | |
| "M:Y_ESMI", | |
| "M:Y_ESMN", | |
| "M:Y_ESMP", | |
| "M:Y_ESMV", | |
| "M:Y_HELP", | |
| "M:Y_SPECIAL", | |
| "M:Y_SSCCV", | |
| "M:Y_SSCM", | |
| "M:Y_SSMA", | |
| "M:Y_SSMAdvP", | |
| "M:Y_SSMI", | |
| "M:Y_SSMN", | |
| "M:Y_SSMP", | |
| "M:Y_SSMV", | |
| "M:Y_STQ", | |
| ] | |
| SSCCVspan_label_list = [ | |
| "AFTER_ADJECTIVAL", | |
| "AFTER_ADVERBIAL", | |
| "AFTER_CLEFT_CLAUSE", | |
| "AFTER_COGNITIVE_COMMUNICATIVE_VP", | |
| "AFTER_COMPLEX_NP", | |
| "AFTER_COMPLEX_PHRASE", | |
| "AFTER_FREE_RELATIVE_CLAUSE", | |
| "AFTER_INTENSIFYING_CLAUSE", | |
| "AFTER_REPORTING_CLAUSE", | |
| "AFTER_RESTRICTIVE_CLAUSE_GENERIC_HEAD", | |
| "AFTER_WH_PHRASE", | |
| "BEFORE_ADJECTIVAL", | |
| "BEFORE_ADVERBIAL", | |
| "BEFORE_CLEFT_CLAUSE", | |
| "BEFORE_COGNITIVE_COMMUNICATIVE_VP", | |
| "BEFORE_COMPLEX_NP", | |
| "BEFORE_COMPLEX_PHRASE", | |
| "BEFORE_FREE_RELATIVE_CLAUSE", | |
| "BEFORE_INTENSIFYING_CLAUSE", | |
| "BEFORE_REPORTING_CLAUSE", | |
| "BEFORE_RESTRICTIVE_CLAUSE_GENERIC_HEAD", | |
| "BEFORE_WH_PHRASE", | |
| "ERROR", | |
| "IN_ADJECTIVAL", | |
| "IN_ADJECTIVAL_AFTERSIGN", | |
| "IN_ADJECTIVAL_BEFORESIGN", | |
| "IN_ADVERBIAL", | |
| "IN_ADVERBIAL_AFTERSIGN", | |
| "IN_ADVERBIAL_BEFORESIGN", | |
| "IN_CLEFT_CLAUSE", | |
| "IN_CLEFT_CLAUSE_AFTERSIGN", | |
| "IN_CLEFT_CLAUSE_BEFORESIGN", | |
| "IN_COGNITIVE_COMMUNICATIVE_VP", | |
| "IN_COGNITIVE_COMMUNICATIVE_VP_AFTERSIGN", | |
| "IN_COGNITIVE_COMMUNICATIVE_VP_BEFORESIGN", | |
| "IN_COMPLEX_NP", | |
| "IN_COMPLEX_NP_AFTERSIGN", | |
| "IN_COMPLEX_NP_BEFORESIGN", | |
| "IN_COMPLEX_PHRASE", | |
| "IN_COMPLEX_PHRASE_AFTERSIGN", | |
| "IN_COMPLEX_PHRASE_BEFORESIGN", | |
| "IN_FREE_RELATIVE_CLAUSE", | |
| "IN_FREE_RELATIVE_CLAUSE_AFTERSIGN", | |
| "IN_FREE_RELATIVE_CLAUSE_BEFORESIGN", | |
| "IN_INTENSIFYING_CLAUSE", | |
| "IN_INTENSIFYING_CLAUSE_AFTERSIGN", | |
| "IN_INTENSIFYING_CLAUSE_BEFORESIGN", | |
| "IN_REPORTING_CLAUSE_AFTERSIGN", | |
| "IN_REPORTING_CLAUSE_BEFORESIGN", | |
| "IN_RESTRICTIVE_CLAUSE_GENERIC_HEAD", | |
| "IN_RESTRICTIVE_CLAUSE_GENERIC_HEAD_AFTERSIGN", | |
| "IN_RESTRICTIVE_CLAUSE_GENERIC_HEAD_BEFORESIGN", | |
| "IN_WH_PHRASE", | |
| "IN_WH_PHRASE_AFTERSIGN", | |
| "IN_WH_PHRASE_BEFORESIGN", | |
| "UNKNOWN", | |
| ] | |
| SSMNspan_label_list = [ | |
| "AFTER_COMPLEX_NP", | |
| "BEFORE_COMPLEX_NP", | |
| "ERROR", | |
| "IN_COMPLEX_NP", | |
| "IN_COMPLEX_NP_AFTERSIGN", | |
| "IN_COMPLEX_NP_BEFORESIGN", | |
| "UNKNOWN", | |
| ] | |
| CCVspan_label_list = [ | |
| "AFTER_COMPOUND", | |
| "BEFORE_COMPOUND", | |
| "IN_COMPOUND", | |
| "IN_COMPOUND_AFTERSIGN", | |
| "IN_COMPOUND_BEFORESIGN", | |
| "NOT_CLAUSE_COORDINATOR", | |
| "UNKNOWN", | |
| ] | |
| CMV1span_label_list = [ | |
| "AFTER_COMPOUND", | |
| "BEFORE_COMPOUND", | |
| "IN_COMPOUND", | |
| "IN_COMPOUND_AFTERSIGN", | |
| "IN_COMPOUND_BEFORESIGN", | |
| "NOT_VP_COORDINATOR", | |
| "UNKNOWN", | |
| ] | |
| CMN1span_label_list = [ | |
| "AFTER_COMPOUND", | |
| "BEFORE_COMPOUND", | |
| "IN_COMPOUND", | |
| "IN_COMPOUND_AFTERSIGN", | |
| "IN_COMPOUND_BEFORESIGN", | |
| "NOT_NP_COORDINATOR", | |
| "UNKNOWN", | |
| ] | |
| CLNspan_label_list = [ | |
| "AFTER_COMPOUND", | |
| "BEFORE_COMPOUND", | |
| "IN_COMPOUND", | |
| "IN_COMPOUND_AFTERSIGN", | |
| "IN_COMPOUND_BEFORESIGN", | |
| "NOT_NOUN_COORDINATOR", | |
| "UNKNOWN", | |
| ] | |
| required_sign_tags = [ | |
| "M:Y_SSCCV", | |
| "M:Y_SSMN", | |
| "M:Y_CCV", | |
| "M:Y_CMV1", | |
| "M:Y_CMP", | |
| "M:Y_CMN1", | |
| "M:Y_CLN", | |
| "M:Y_CLV", | |
| "M:Y_CLP", | |
| ] | |
| ############################################################################### | |
| # Helper functions (refactored from notebook) | |
| ############################################################################### | |
| ############################################################################### | |
| # Third-person (and similar) pronouns that usually need an antecedent. | |
| # Omit I/you/me — deictic, not anaphoric in the same way. | |
| _ANAPHORIC_PRONOUNS = { | |
| "he", "she", "it", "they", | |
| "him", "her", "them", | |
| # "we", "us", # optional; remove if you prefer | |
| } | |
| def sentence_has_anaphoric_pronoun(sent: str) -> bool: | |
| tokens = re.findall(r"[A-Za-z']+", (sent or "").lower()) | |
| return any(t in _ANAPHORIC_PRONOUNS for t in tokens) | |
| def sentence_entity_key(sent: str) -> str: | |
| """Primary entity ≈ subject head, lowercased lemma/text.""" | |
| doc = nlp(sent) | |
| subj = next((t for t in doc if t.dep_ in ("nsubj", "nsubjpass")), None) | |
| if subj is None: | |
| subj = next((t for t in doc if t.pos_ in ("PROPN", "NOUN", "PRON")), None) | |
| if subj is None: | |
| return sent.lower()[:40] | |
| # Use lemma for "man"/"men"; text for names | |
| if subj.pos_ == "PROPN": | |
| return subj.text.lower() | |
| return subj.lemma_.lower() | |
| def is_pronoun_sentence(sent: str) -> bool: | |
| doc = nlp(sent) | |
| for t in doc: | |
| if t.dep_ in ("nsubj", "nsubjpass") and t.lower_ in PRONOUNS: | |
| return True | |
| return bool(re.search(r"\b(he|she|it|they|him|her|them)\b", sent, re.I)) | |
| def is_identifying(sent: str) -> bool: | |
| """Property / location / copula — good 'given' material.""" | |
| return bool( | |
| re.search( | |
| r"\b(is|are|was|were|lives?|lived|lives in|is a|is an)\b", | |
| sent, | |
| re.I, | |
| ) | |
| ) | |
| def order_sentences_for_discourse(sents): | |
| """ | |
| 1. Pronoun sentences last (antecedent first). | |
| 2. Group remaining sentences by subject entity. | |
| 3. Within a group: identifying sentences before event sentences. | |
| 4. Group order = first appearance in the current list | |
| (≈ order derived from the source sentence). | |
| """ | |
| cleaned = [s.strip() for s in sents if s and s.strip()] | |
| if len(cleaned) <= 1: | |
| return cleaned | |
| pronoun_sents = [] | |
| lexical_sents = [] | |
| for s in cleaned: | |
| (pronoun_sents if is_pronoun_sentence(s) else lexical_sents).append(s) | |
| groups = defaultdict(list) | |
| order = [] | |
| for s in lexical_sents: | |
| key = sentence_entity_key(s) | |
| if key not in groups: | |
| order.append(key) | |
| groups[key].append(s) | |
| ordered = [] | |
| for key in order: | |
| chunk = groups[key] | |
| ident = [s for s in chunk if is_identifying(s)] | |
| rest = [s for s in chunk if s not in ident] | |
| ordered.extend(ident + rest) | |
| ordered.extend(pronoun_sents) | |
| return ordered | |
| ############################################################################### | |
| ############################################################################### | |
| def split_into_sentences(text): | |
| """ | |
| Split plain text into sentences using spaCy. | |
| Falls back to a conservative regex if spaCy returns a single | |
| span that still contains '. ' + capital letter. | |
| """ | |
| text = (text or "").strip() | |
| if not text: | |
| return [] | |
| blocks = [b.strip() for b in text.split("\n") if b.strip()] | |
| sents = [] | |
| for block in blocks: | |
| doc = nlp(block) | |
| piece = [s.text.strip() for s in doc.sents if s.text.strip()] | |
| # Fallback: spaCy sometimes keeps two sentences as one span | |
| expanded = [] | |
| for p in piece: | |
| parts = re.split(r'(?<=[.!?])\s+(?=[A-Z])', p) | |
| expanded.extend(x.strip() for x in parts if x.strip()) | |
| sents.extend(expanded) | |
| # de-dupe while preserving order (can happen with odd whitespace) | |
| seen = set() | |
| unique = [] | |
| for s in sents: | |
| key = re.sub(r"\s+", " ", s.lower()) | |
| if key not in seen: | |
| seen.add(key) | |
| unique.append(s) | |
| return unique | |
| ############################################################################### | |
| ############################################################################### | |
| def strip_xml_tags(text): | |
| """Remove all <M:Y_...> and </M:Y_...> tags.""" | |
| text = re.sub(r'<M:Y_[^>]+>', '', text) | |
| text = re.sub(r'</M:Y_[^>]+>', '', text) | |
| text = re.sub(r'\s+', ' ', text).strip() | |
| return text | |
| ############################################################################### | |
| ############################################################################### | |
| def clean_for_spaCy(sent): | |
| print("CLEANING sent BEFORE spaCy processes it", sent) | |
| # Single [...] region; no (.|\s)* — avoids ReDoS | |
| # Supports: [,], [and], [who], [, who], [, and], [,_and] (legacy) | |
| m = re.search( | |
| r"^(.*?)" | |
| r"\[" | |
| r"(" | |
| r"[,;:(]|" | |
| r"and|but|or|that|what|when|where|which|while|who" | |
| r")" | |
| r"(" | |
| r"_(?:and|but|or|that|what|when|where|which|while|who)|" | |
| r"\s+(?:and|but|or|that|what|when|where|which|while|who)" | |
| r")?" | |
| r"\]" | |
| r"(.*)$", | |
| sent, | |
| flags=re.IGNORECASE | re.DOTALL, | |
| ) | |
| if m: | |
| prefix = m.group(1) | |
| head = m.group(2) | |
| extra = m.group(3) or "" | |
| suffix = m.group(4) | |
| if extra.startswith("_"): | |
| # legacy bigram: ,_and → ,_and inside brackets | |
| inside = head + extra | |
| elif extra: | |
| # [, who] / [, and] | |
| inside = f"{head} {extra.strip()}" | |
| else: | |
| inside = head | |
| clean_sent = f"{prefix}[{inside}]{suffix}" | |
| else: | |
| clean_sent = sent | |
| clean_sent = re.sub(r"\s+", " ", clean_sent).strip() | |
| print("clean_sent is\n" + clean_sent) | |
| return clean_sent | |
| ############################################################################### | |
| ############################################################################### | |
| # Tidying up predicted tags involving [ and ]. | |
| def sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ): | |
| clean_tokens = [] | |
| clean_pos_tags = [] | |
| clean_lemmas = [] | |
| clean_predictions = [] | |
| x = 0 | |
| while x < len(aligned_tokens): | |
| # and | |
| if aligned_tokens[x] == "[" and aligned_tokens[x + 1] == ":]": | |
| clean_predictions.append(aligned_predictions[x]) | |
| clean_tokens.append(":") | |
| clean_pos_tags.append(":") | |
| clean_lemmas.append(":") | |
| x += 1 | |
| elif aligned_tokens[x] == "[" and aligned_tokens[x + 2] == "]": | |
| clean_predictions.append(aligned_predictions[x]) | |
| clean_tokens.append(aligned_tokens[x + 1]) | |
| clean_pos_tags.append(aligned_pos_tags[x + 1]) | |
| clean_lemmas.append(aligned_lemmas[x + 1]) | |
| x += 1 | |
| # comma-and | |
| elif aligned_tokens[x] == "[" and aligned_tokens[x + 4] == "]": | |
| clean_predictions.append(aligned_predictions[x]) | |
| clean_predictions.append(aligned_predictions[x]) | |
| clean_predictions.append(aligned_predictions[x]) | |
| clean_pos_tags.append(aligned_pos_tags[x + 1]) | |
| clean_pos_tags.append(aligned_pos_tags[x + 2]) | |
| clean_pos_tags.append(aligned_pos_tags[x + 3]) | |
| clean_lemmas.append(aligned_lemmas[x + 1]) | |
| clean_lemmas.append(aligned_lemmas[x + 2]) | |
| clean_lemmas.append(aligned_lemmas[x + 3]) | |
| clean_tokens.append(aligned_tokens[x + 1]) | |
| clean_tokens.append(aligned_tokens[x + 2]) | |
| clean_tokens.append(aligned_tokens[x + 3]) | |
| x += 3 | |
| elif aligned_tokens[x] == "]" and aligned_tokens[x - 2] == "[": | |
| True | |
| elif aligned_tokens[x] == "]" and aligned_tokens[x - 4] == "[": | |
| True | |
| else: | |
| clean_tokens.append(aligned_tokens[x]) | |
| clean_pos_tags.append(aligned_pos_tags[x]) | |
| clean_lemmas.append(aligned_lemmas[x]) | |
| clean_predictions.append(aligned_predictions[x]) | |
| x += 1 | |
| new_clean_predictions = improve_predicted_tag_sequence(clean_predictions) | |
| clean_predictions = new_clean_predictions | |
| return (clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions) | |
| ############################################################################### | |
| ############################################################################### | |
| def improve_predicted_tag_sequence(predictions): | |
| new_predictions = [] | |
| # print('IMPROVING PREDICTED TAG SEQUENCE') | |
| # print(predictions) | |
| core_tags = [] | |
| x = 0 | |
| sign_position = False | |
| while x < len(predictions): | |
| px = predictions[x] | |
| core_tag = re.sub( | |
| "(IN_|BEFORE_|AFTER_|_AFTERSIGN|_BEFORESIGN)", | |
| "", | |
| px, | |
| ) | |
| core_tags.append(core_tag) | |
| x += 1 | |
| most_frequent_core_tag = max(set(core_tags), key=core_tags.count) | |
| sign_tag = re.compile(r"IN_" + most_frequent_core_tag + "$") | |
| x = 0 | |
| while x < len(predictions): | |
| px = predictions[x] | |
| if re.match( | |
| sign_tag, | |
| px, | |
| ): | |
| sign_position = x | |
| break | |
| x += 1 | |
| improved_predicted_tag_sequence = [] | |
| x = 0 | |
| while x < len(predictions): | |
| px = predictions[x] | |
| if x < sign_position: | |
| if px == "UNKNOWN" or px == "NOT_CLAUSE_COORDINATOR": | |
| px = "IN_" + core_tag + "_BEFORESIGN" | |
| improved_predicted_tag_sequence.append(px) | |
| x += 1 | |
| predictions = improved_predicted_tag_sequence | |
| # print('improved_predicted_tag_sequence:', improved_predicted_tag_sequence) | |
| x = 0 | |
| previous_prediction = "" | |
| while x < len(predictions): | |
| if x > 0: | |
| if ( | |
| predictions[x] == "UNKNOWN" | |
| or predictions[x] == "NOT_CLAUSE_COORDINATOR" | |
| ): | |
| # new_predictions.append(previous_prediction) | |
| try: | |
| new_predictions.append(new_predictions[-1]) | |
| except: | |
| new_predictions.append(predictions[x]) | |
| else: | |
| new_predictions.append(predictions[x]) | |
| else: | |
| new_predictions.append(predictions[x]) | |
| x += 1 | |
| # print('new_predictions:', new_predictions) | |
| return new_predictions | |
| ############################################################################### | |
| ############################################################################### | |
| def indefinite_article_for(word: str) -> str: | |
| """Choose a/an from the following orthographic word.""" | |
| if not word: | |
| return "a" | |
| w = re.sub(r"[^A-Za-z]", "", word).lower() | |
| if not w: | |
| return "a" | |
| # simple orthographic rule (good enough for lecturer, hour, university, …) | |
| if w[0] in "aeiou": | |
| return "an" | |
| return "a" | |
| ############################################################################### | |
| ############################################################################### | |
| def make_indefinite_np(np_text: str) -> str: | |
| """ | |
| the famous lecturer → a famous lecturer | |
| an old friend → an old friend (unchanged) | |
| famous lecturer → a famous lecturer (if bare, add article) | |
| """ | |
| np_text = re.sub(r"\s+", " ", (np_text or "")).strip() | |
| if not np_text: | |
| return np_text | |
| m = re.match(r"^(the|a|an)\s+(.*)$", np_text, flags=re.I) | |
| if m: | |
| rest = m.group(2).strip() | |
| first = rest.split()[0] if rest else "" | |
| return f"{indefinite_article_for(first)} {rest}".strip() | |
| first = np_text.split()[0] | |
| return f"{indefinite_article_for(first)} {np_text}".strip() | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_SSCCV_span(sentence): | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| encoded = SSCCVspanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (Windows CPU or HF GPU/ZeroGPU) | |
| model_device = next(SSCCVspanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = SSCCVspanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = SSCCVspanTaggingTokenizer.tokenize( | |
| SSCCVspanTaggingTokenizer.decode( | |
| SSCCVspanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| SSCCV_span_tagged_predictions = [ | |
| SSCCVspan_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU) --- | |
| aligned_tokens = [] | |
| aligned_predictions = [] | |
| aligned_pos_tags = [] | |
| aligned_lemmas = [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| print("Checking proper nouns after building doc") | |
| for t in doc: | |
| if "NNP" in t.tag_: | |
| print(t.tag_, t.text) | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(SSCCV_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(SSCCV_span_tagged_predictions[j]) | |
| break | |
| clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions = sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| simplifiable = all("COMPLEX_NP" in p for p in clean_predictions) | |
| if not simplifiable: | |
| True # placeholder for future logic | |
| return ( | |
| clean_tokens, | |
| clean_pos_tags, | |
| clean_lemmas, | |
| clean_predictions, | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_SSMN_span(sentence): | |
| """ | |
| sentence must contain the left appositive boundary in brackets, e.g.: | |
| John[,] the famous teacher, went home. | |
| """ | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| # Prefer the full call so you get input_ids + attention_mask | |
| encoded = SSMNspanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Put every tensor on the *same* device as the model | |
| model_device = next(SSMNspanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = SSMNspanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| # Wordpiece tokens for alignment (same string as above) | |
| tokens = SSMNspanTaggingTokenizer.tokenize( | |
| SSMNspanTaggingTokenizer.decode( | |
| SSMNspanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| ssmn_span_tagged_predictions = [ | |
| SSMNspan_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU is fine) --- | |
| aligned_tokens, aligned_predictions = [], [] | |
| aligned_pos_tags, aligned_lemmas = [], [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(ssmn_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(ssmn_span_tagged_predictions[j]) | |
| break | |
| return sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_CCV_span(sentence): | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| encoded = CCVspanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (Windows CPU or HF GPU/ZeroGPU) | |
| model_device = next(CCVspanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = CCVspanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = CCVspanTaggingTokenizer.tokenize( | |
| CCVspanTaggingTokenizer.decode( | |
| CCVspanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| CCV_span_tagged_predictions = [ | |
| CCVspan_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU) --- | |
| aligned_tokens = [] | |
| aligned_predictions = [] | |
| aligned_pos_tags = [] | |
| aligned_lemmas = [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(CCV_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(CCV_span_tagged_predictions[j]) | |
| break | |
| clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions = sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| simplifiable = all("COMPOUND" in p for p in clean_predictions) | |
| if not simplifiable: | |
| True # placeholder for future logic | |
| return ( | |
| clean_tokens, | |
| clean_pos_tags, | |
| clean_lemmas, | |
| clean_predictions, | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_CMV1_span(sentence): | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| encoded = CMV1spanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (Windows CPU or HF GPU/ZeroGPU) | |
| model_device = next(CMV1spanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = CMV1spanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = CMV1spanTaggingTokenizer.tokenize( | |
| CMV1spanTaggingTokenizer.decode( | |
| CMV1spanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| CMV1_span_tagged_predictions = [ | |
| CMV1span_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU) --- | |
| aligned_tokens = [] | |
| aligned_predictions = [] | |
| aligned_pos_tags = [] | |
| aligned_lemmas = [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(CMV1_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(CMV1_span_tagged_predictions[j]) | |
| break | |
| clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions = sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| simplifiable = all("COMPOUND" in p for p in clean_predictions) | |
| if not simplifiable: | |
| True # placeholder for future logic | |
| return ( | |
| clean_tokens, | |
| clean_pos_tags, | |
| clean_lemmas, | |
| clean_predictions, | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_CMN1_span(sentence): | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| encoded = CMN1spanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (Windows CPU or HF GPU/ZeroGPU) | |
| model_device = next(CMN1spanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = CMN1spanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = CMN1spanTaggingTokenizer.tokenize( | |
| CMN1spanTaggingTokenizer.decode( | |
| CMN1spanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| CMN1_span_tagged_predictions = [ | |
| CMN1span_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU) --- | |
| aligned_tokens = [] | |
| aligned_predictions = [] | |
| aligned_pos_tags = [] | |
| aligned_lemmas = [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(CMN1_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(CMN1_span_tagged_predictions[j]) | |
| break | |
| clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions = sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| simplifiable = all("COMPOUND" in p for p in clean_predictions) | |
| if not simplifiable: | |
| True # placeholder for future logic | |
| return ( | |
| clean_tokens, | |
| clean_pos_tags, | |
| clean_lemmas, | |
| clean_predictions, | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_CLN_span(sentence): | |
| if re.search(r"\[\:\]\s*$", sentence): | |
| sentence = sentence + " blah blah blah." | |
| # --- BERT side --- | |
| encoded = CLNspanTaggingTokenizer( | |
| sentence, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (works on Windows CPU and HF GPU/ZeroGPU) | |
| model_device = next(CLNspanTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = CLNspanTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = CLNspanTaggingTokenizer.tokenize( | |
| CLNspanTaggingTokenizer.decode( | |
| CLNspanTaggingTokenizer.encode(sentence) | |
| ) | |
| ) | |
| CLN_span_tagged_predictions = [ | |
| CLNspan_label_list[p] for p in predictions[0].tolist() | |
| ] | |
| # --- spaCy side (CPU) --- | |
| aligned_tokens = [] | |
| aligned_predictions = [] | |
| aligned_pos_tags = [] | |
| aligned_lemmas = [] | |
| sc_sentence = ( | |
| clean_for_spaCy(sentence) | |
| if "[" in sentence and "]" in sentence | |
| else sentence | |
| ) | |
| doc = nlp(sc_sentence) | |
| spacy_pos_tags = [t.tag_ for t in doc] | |
| spacy_tokens = [t.text for t in doc] | |
| spacy_lemmas = [t.lemma_ for t in doc] | |
| a2b, b2a = tokenizations.get_alignments(spacy_tokens, tokens) | |
| for i in range(len(spacy_tokens)): | |
| for j in a2b[i]: | |
| if j < len(CLN_span_tagged_predictions): | |
| aligned_tokens.append(spacy_tokens[i]) | |
| aligned_pos_tags.append(spacy_pos_tags[i]) | |
| aligned_lemmas.append(spacy_lemmas[i]) | |
| aligned_predictions.append(CLN_span_tagged_predictions[j]) | |
| break | |
| clean_tokens, clean_pos_tags, clean_lemmas, clean_predictions = sign_cleaning( | |
| aligned_tokens, aligned_pos_tags, aligned_lemmas, aligned_predictions | |
| ) | |
| simplifiable = all("COMPOUND" in p for p in clean_predictions) | |
| if not simplifiable: | |
| True # placeholder for future logic | |
| return ( | |
| clean_tokens, | |
| clean_pos_tags, | |
| clean_lemmas, | |
| clean_predictions, | |
| ) | |
| ############################################################################### | |
| ############################################################################### | |
| def merge_wordpieces(sign_tagged_tokens): | |
| """ | |
| Merge BERT WordPiece tokens back into whole words. | |
| Example: [('ma', 'M:N_CCV'), ('##ry', 'M:N_CCV')] → [('mary', 'M:N_CCV')] | |
| """ | |
| merged = [] | |
| for tok, lab in sign_tagged_tokens: | |
| if tok in ('[CLS]', '[SEP]'): | |
| continue | |
| if tok.startswith('##') and merged: | |
| prev_tok, prev_lab = merged[-1] | |
| merged[-1] = (prev_tok + tok[2:], prev_lab) | |
| else: | |
| # also strip a lone '#' that sometimes appears after bad cleaning | |
| if tok in ('#', '##'): | |
| continue | |
| merged.append((tok, lab)) | |
| return merged | |
| ############################################################################### | |
| ############################################################################### | |
| def tag_all_signs_in_sent(sent): | |
| """Returns a LIST of strings with <M:Y_...> tags inserted.""" | |
| sent = re.sub("do not", "don't", sent) | |
| # Remove any existing square brackets around signs | |
| sent = re.sub( | |
| r"\[((,|;|:|and|but|or|that|what|when|where|which|while|who)" | |
| r"(\_(and|but|or|that|what|when|where|which|while|who))?)\]", | |
| r"\1\2", | |
| sent, | |
| flags=re.IGNORECASE, | |
| ) | |
| # --- BERT side --- | |
| encoded = SignTaggingTokenizer( | |
| sent, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=512, | |
| ) | |
| # Same device as the model (Windows CPU or HF GPU/ZeroGPU) | |
| model_device = next(SignTaggingModel.parameters()).device | |
| encoded = {k: v.to(model_device) for k, v in encoded.items()} | |
| with torch.no_grad(): | |
| outputs = SignTaggingModel(**encoded)[0] | |
| predictions = torch.argmax(outputs, dim=2) | |
| tokens = SignTaggingTokenizer.tokenize( | |
| SignTaggingTokenizer.decode(SignTaggingTokenizer.encode(sent)) | |
| ) | |
| sign_tagged_tokens = [ | |
| (token, sign_label_list[prediction]) | |
| for token, prediction in zip(tokens, predictions[0].tolist()) | |
| ] | |
| # Merge sub-word pieces before any further processing | |
| sign_tagged_tokens = merge_wordpieces(sign_tagged_tokens) | |
| print("sign_tagged_tokens:", sign_tagged_tokens) | |
| relevant_sign_offsets = [] | |
| relevant_tags = [] | |
| t = 0 | |
| while t < len(sign_tagged_tokens): | |
| lab = sign_tagged_tokens[t][1] | |
| if re.match("M:Y", lab): | |
| relevant_sign_offsets.append(t) | |
| relevant_tags.append(lab) | |
| t += 1 | |
| tagged_sent_list = [tok for tok, _ in sign_tagged_tokens] | |
| x = 0 | |
| while x < len(relevant_sign_offsets): | |
| o = relevant_sign_offsets[x] | |
| rtag = relevant_tags[x] | |
| tagged_sent_list[o] = ( | |
| "<" + rtag + ">" + tagged_sent_list[o] + "</" + rtag + ">" | |
| ) | |
| x += 1 | |
| # Build the final list (excluding [CLS] and [SEP]) | |
| tagged_sent = [] | |
| x = 0 | |
| while x < len(tagged_sent_list): | |
| if sign_tagged_tokens[x][0] not in ["[CLS]", "[SEP]"]: | |
| tagged_sent.append(tagged_sent_list[x]) | |
| x += 1 | |
| print(f"Returning tagged_sent: result = {tagged_sent}") | |
| return tagged_sent # LIST, not a string | |
| ############################################################################### | |
| ############################################################################### | |
| def split_coordinated_relative_body(body): | |
| """ | |
| Split 'Steven kicked and who Mary liked' | |
| -> ['Steven kicked', 'Mary liked'] | |
| Also handles 'and which', bare 'and' between relative-like conjuncts. | |
| """ | |
| body = body.strip() | |
| # Prefer explicit repeated relative marker | |
| parts = re.split( | |
| r"\s+and\s+(?:who|which|whom|that)\s+", | |
| body, | |
| flags=re.IGNORECASE, | |
| ) | |
| if len(parts) >= 2: | |
| return [p.strip() for p in parts if p.strip()] | |
| return [body] | |
| ############################################################################### | |
| ############################################################################### | |
| def _region_text(elem): | |
| if elem is None: | |
| return "" | |
| parts = [] | |
| for w in elem.findall("W"): | |
| if w.text and str(w.text).strip(): | |
| parts.append(str(w.text).strip()) | |
| return " ".join(parts).strip() | |
| ############################################################################### | |
| ############################################################################### | |
| def _split_coord_relative_body(body): | |
| """ | |
| 'Steven kicked and who Mary liked' | |
| -> ['Steven kicked', 'Mary liked'] | |
| """ | |
| body = re.sub(r"\s+", " ", (body or "")).strip() | |
| if not body: | |
| return [] | |
| parts = re.split( | |
| r"\s+and\s+(?:who|which|whom|that)\s+", | |
| body, | |
| flags=re.IGNORECASE, | |
| ) | |
| parts = [p.strip() for p in parts if p.strip()] | |
| return parts if parts else [body] | |
| ############################################################################### | |
| ############################################################################### | |
| def _tidy_plain(s): | |
| s = re.sub(r"\s+", " ", (s or "")).strip() | |
| s = re.sub(r"\s+([.,!?;:])", r"\1", s) | |
| s = re.sub(r",+\s*\.", ".", s) | |
| s = re.sub(r",+\s*$", "", s) | |
| if s and not s.endswith((".", "!", "?")): | |
| s += "." | |
| if s: | |
| s = s[0].upper() + s[1:] | |
| return s | |
| ############################################################################### | |
| # Placeholder for simplification functions (core of the 1501 lines; implement from original) | |
| ############################################################################### | |
| def SSCCVsimplify(xml_sent): | |
| simplification = [] | |
| xml_sent2 = xml_sent | |
| xml_string = ET.tostring(xml_sent).decode("utf-8") | |
| xml_for_printing = xml.dom.minidom.parseString(xml_string) | |
| pretty_xml_as_string = xml_for_printing.toprettyxml() | |
| # print(pretty_xml_as_string) | |
| simp_sent1 = ET.Element("S") | |
| simp_sent2 = ET.Element("S") | |
| if xml_sent.attrib["TYPE"] == "COMPLEX_NP": | |
| complex_NP_type = get_COMPLEX_NP_type(xml_sent) | |
| print("SSCCV complex_NP_type:", complex_NP_type) | |
| # print('SSCCV SIMPLIFYING:', complex_NP_type + '\nINPUT SENTENCE:\n'+ pretty_xml_as_string, file=sys.stderr) | |
| ####################################################################### | |
| ####################################################################### | |
| ####################################################################### | |
| if complex_NP_type == "SUBJECT_RELATIVISED": | |
| # Subject relative, e.g.: | |
| # John, who lives in London, went home. | |
| # John, who lives in London and who works in Paris, went home. | |
| # | |
| # Matrix: [BEFORE] + head + [AFTER] | |
| # Relative: head + each conjunct (predication) | |
| # | |
| # Coordinated body "lives in London and who works in Paris" | |
| # -> ["lives in London", "works in Paris"] | |
| # -> "John lives in London." / "John works in Paris." | |
| before_el = xml_sent.find("BEFORE_COMPLEX_CONSTITUENT") | |
| head_el = xml_sent.find("IN_COMPLEX_CONSTITUENT_BEFORESIGN") | |
| body_el = xml_sent.find("IN_COMPLEX_CONSTITUENT_AFTERSIGN") | |
| after_el = xml_sent.find("AFTER_COMPLEX_CONSTITUENT") | |
| head = re.sub(r"[,\s]+$", "", _region_text(head_el)).strip() | |
| body = re.sub(r"^[,\s]+", "", _region_text(body_el)).strip() | |
| after = re.sub(r"^[,\s]+", "", _region_text(after_el)).strip() | |
| before = _region_text(before_el) | |
| matrix = _tidy_plain(" ".join(x for x in [before, head, after] if x)) | |
| conjuncts = _split_coord_relative_body(body) | |
| out = [] | |
| if matrix: | |
| out.append(matrix) | |
| for conj in conjuncts: | |
| if conj and head: | |
| out.append(_tidy_plain(f"{head} {conj}")) | |
| return out | |
| ####################################################################### | |
| ####################################################################### | |
| ####################################################################### | |
| # ''' | |
| # [John saw] [the bucket] [into] [which] [the coin had fallen][]. | |
| # [John saw] [the bucket][]. | |
| # [The coin had fallen] [into] [the bucket] | |
| # | |
| # ''' | |
| elif complex_NP_type == "PREPOSITION_OBJECT_RELATIVISED": | |
| copula = "" | |
| xml_prep = ET.Element("W") | |
| for vor_sent_el in xml_sent: | |
| if vor_sent_el.tag == "IN_COMPLEX_CONSTITUENT_BEFORESIGN": | |
| for vor_sent_subel in vor_sent_el: | |
| if vor_sent_subel.attrib["POS"] == "IN": | |
| xml_prep = vor_sent_subel | |
| vor_sent_matrix_subject = ET.Element("NA") | |
| vor_sent_el_bcc = ET.Element("NA") | |
| vor_sent_prep = ET.Element("NA") | |
| vor_sent_acc = ET.Element("NA") | |
| vor_sent2_prep = ET.Element("NA") | |
| vor_sent2_matrix_subject = ET.Element("NA") | |
| vor_sent2_clause = ET.Element("NA") | |
| for vor_sent_el in xml_sent: | |
| if vor_sent_el.tag == "IN_COMPLEX_CONSTITUENT": | |
| try: | |
| if re.match("(\,|\:|\;)$", vor_sent_el[-2].text): | |
| punctuation_boundary = True | |
| except: | |
| True | |
| # DELETING STRAY COMMA IMMEDIATELY PRECEDING THE SIGN DETECTED BY THE BERT MODEL | |
| elif vor_sent_el.tag == "IN_COMPLEX_CONSTITUENT_BEFORESIGN": | |
| if re.match("(\,|\:|\;)$", vor_sent_el[-1].text): | |
| del vor_sent_el[-1] | |
| # DELETING STRAY COMMA IMMEDIATELY FOLLOWING THE SIGN DETECTED BY THE BERT MODEL | |
| elif vor_sent_el.tag == "AFTER_COMPLEX_CONSTITUENT": | |
| if re.match("(\,|\:|\;)$", vor_sent_el[0].text): | |
| del vor_sent_el[0] | |
| for vor_sent_el in xml_sent: | |
| if vor_sent_el.tag == "BEFORE_COMPLEX_CONSTITUENT": | |
| vor_sent_el_bcc = vor_sent_el | |
| elif vor_sent_el.tag == "IN_COMPLEX_CONSTITUENT_BEFORESIGN": | |
| vor_sent_prep = xml_prep | |
| vor_sent2_prep = xml_prep | |
| vor_sent_matrix_subject = vor_sent_el | |
| vor_sent_matrix_subject.remove(vor_sent_matrix_subject[-1]) | |
| elif vor_sent_el.tag == "AFTER_COMPLEX_CONSTITUENT": | |
| if re.match("(\,|\:|\;)$", vor_sent_el[0].text): | |
| del vor_sent_el[0] | |
| vor_sent_acc = vor_sent_el | |
| simp_sent1.append(vor_sent_el_bcc) | |
| simp_sent1.append(vor_sent_matrix_subject) | |
| simp_sent1.append(vor_sent_acc) | |
| for vor_sent2_el in xml_sent2: | |
| if vor_sent2_el.tag == "IN_COMPLEX_CONSTITUENT_BEFORESIGN": | |
| try: | |
| if vor_sent2_el[-1].attrib["POS"] == "IN": | |
| vor_sent2_prep = vor_sent2_el[-1] | |
| vor_sent2_matrix_subject = vor_sent2_el | |
| except: | |
| True | |
| elif vor_sent2_el.tag == "IN_COMPLEX_CONSTITUENT_AFTERSIGN": | |
| vor_sent2_clause = vor_sent2_el | |
| simp_sent2.append(vor_sent2_clause) | |
| simp_sent2.append(vor_sent2_prep) | |
| simp_sent2.append(vor_sent2_matrix_subject) | |
| ####################################################################### | |
| ####################################################################### | |
| ####################################################################### | |
| # ''' | |
| # [John saw] [the bucket] [which] [Peter despised][]. | |
| # [John saw] [the bucket][]. | |
| # [Peter despised] [the bucket]. | |
| # | |
| # ''' | |
| elif complex_NP_type == "VERB_OBJECT_RELATIVISED": | |
| # Object relative, e.g.: | |
| # John, who Steven kicked, went home. | |
| # John, who Steven kicked and who Mary liked, went home. | |
| # | |
| # Matrix: head + AFTER | |
| # Relative(s): each conjunct + head as object | |
| before_el = xml_sent.find("BEFORE_COMPLEX_CONSTITUENT") | |
| head_el = xml_sent.find("IN_COMPLEX_CONSTITUENT_BEFORESIGN") | |
| body_el = xml_sent.find("IN_COMPLEX_CONSTITUENT_AFTERSIGN") | |
| after_el = xml_sent.find("AFTER_COMPLEX_CONSTITUENT") | |
| head = re.sub(r"[,\s]+$", "", _region_text(head_el)).strip() | |
| body = re.sub(r"^[,\s]+", "", _region_text(body_el)).strip() | |
| after = re.sub(r"^[,\s]+", "", _region_text(after_el)).strip() | |
| before = _region_text(before_el) | |
| matrix = _tidy_plain(" ".join(x for x in [before, head, after] if x)) | |
| conjuncts = _split_coord_relative_body(body) | |
| out = [] | |
| if matrix: | |
| out.append(matrix) | |
| for conj in conjuncts: | |
| if conj and head: | |
| out.append(_tidy_plain(f"{conj} {head}")) | |
| return out # leave function here; do not use simp_sent1/simp_sent2 | |
| if len(simp_sent1) > 0 and len(simp_sent2) > 0: | |
| s1_xml_string = ET.tostring(simp_sent1).decode("utf-8") | |
| s1_xml_for_printing = xml.dom.minidom.parseString(s1_xml_string) | |
| s1_pretty_xml_as_string = s1_xml_for_printing.toprettyxml() | |
| s2_xml_string = ET.tostring(simp_sent2).decode("utf-8") | |
| s2_xml_for_printing = xml.dom.minidom.parseString(s2_xml_string) | |
| s2_pretty_xml_as_string = s2_xml_for_printing.toprettyxml() | |
| """ | |
| Be aware that simp_sent1 and simp_sent2 have a different structure | |
| from xml_sent. This affects the xml_to_text function/process. | |
| """ | |
| try: | |
| simplification.append(simplified_sent_xml_to_text(simp_sent1)) | |
| simplification.append(simplified_sent_xml_to_text(simp_sent2)) | |
| except Exception as e: | |
| print("SSCCVsimplify xml_to_text error:", e) | |
| print("simp_sent1:", ET.tostring(simp_sent1).decode("utf-8")) | |
| print("simp_sent2:", ET.tostring(simp_sent2).decode("utf-8")) | |
| # print('EXITING SSCCVsimplify:', simplification, file=sys.stderr) | |
| return simplification | |
| ############################################################################### | |
| ############################################################################### | |
| def looks_like_proper_name_np(np_text: str) -> bool: | |
| toks = np_text.split() | |
| return bool(toks) and all(t[:1].isupper() for t in toks if t.isalpha()) | |
| ############################################################################### | |
| ############################################################################### | |
| def SSMNsimplify(xml_sent): | |
| """ | |
| Appositive / non-finite nominal modifier (SSMN): | |
| John Smith, the famous lecturer, went home. | |
| → John Smith went home. | |
| → John Smith is a famous lecturer. | |
| """ | |
| simplification = [] | |
| try: | |
| if xml_sent.attrib.get("TYPE") not in ("COMPLEX_NP", "APPOSITIVE", "SSMN"): | |
| # still try if elements exist | |
| pass | |
| def elem_text(elem): | |
| if elem is None: | |
| return "" | |
| parts = [] | |
| for w in elem.findall("W"): | |
| if not w.text: | |
| continue | |
| t = str(w.text).strip() | |
| t = re.sub(r"[\[\]]+", "", t) | |
| if t: | |
| parts.append(t) | |
| return " ".join(parts).strip() | |
| def strip_edge_commas(s: str) -> str: | |
| s = re.sub(r"\s+", " ", (s or "")).strip() | |
| s = re.sub(r"^[,;:\s]+", "", s) | |
| s = re.sub(r"[,;:\s]+$", "", s) | |
| return s.strip() | |
| def tidy_sent(s: str) -> str: | |
| s = re.sub(r"\s+", " ", (s or "")).strip() | |
| s = re.sub(r"\s+([.,!?;:])", r"\1", s) | |
| s = re.sub(r"([.,!?;:])\s+", r"\1 ", s) | |
| s = re.sub(r",+\s*\.", ".", s) | |
| if s and not s.endswith((".", "!", "?")): | |
| s += "." | |
| if s: | |
| s = s[0].upper() + s[1:] | |
| return s | |
| before = elem_text(xml_sent.find(".//BEFORE_COMPLEX_CONSTITUENT")) | |
| head = elem_text(xml_sent.find(".//IN_COMPLEX_CONSTITUENT_BEFORESIGN")) | |
| # trigger comma lives in IN_COMPLEX_CONSTITUENT — discarded | |
| appositive = elem_text(xml_sent.find(".//IN_COMPLEX_CONSTITUENT_AFTERSIGN")) | |
| after = elem_text(xml_sent.find(".//AFTER_COMPLEX_CONSTITUENT")) | |
| head = strip_edge_commas(head) | |
| appositive = strip_edge_commas(appositive) | |
| before = strip_edge_commas(before) | |
| after = strip_edge_commas(after) | |
| if not head: | |
| return [] | |
| # 1) Matrix clause without appositive | |
| matrix = " ".join(x for x in [before, head, after] if x).strip() | |
| matrix = tidy_sent(matrix) | |
| # 2) Equative: Head is a/an … | |
| if looks_like_proper_name_np(appositive): | |
| pred_np = appositive | |
| else: | |
| pred_np = make_indefinite_np(appositive) | |
| equative = tidy_sent(f"{head} is {pred_np}") | |
| if matrix: | |
| simplification.append(matrix) | |
| if equative and equative != matrix: | |
| simplification.append(equative) | |
| print(f"SSMNsimplify produced {len(simplification)} sentences: {simplification}") | |
| return simplification | |
| except Exception as e: | |
| print(f"SSMNsimplify error: {e}") | |
| return [] | |
| ############################################################################### | |
| ############################################################################### | |
| def extract_coordinator_from_xml(xml_sent): | |
| """Surface of IN_COMPOUND_CONSTITUENT (and / or / but / ; / , and …).""" | |
| elem = xml_sent.find(".//IN_COMPOUND_CONSTITUENT") | |
| if elem is None: | |
| return None | |
| parts = [] | |
| for w in elem.findall("W"): | |
| if w.text and str(w.text).strip(): | |
| parts.append(str(w.text).strip()) | |
| if not parts: | |
| return None | |
| raw = " ".join(parts).strip().lower() | |
| raw = re.sub(r"\s+", " ", raw) | |
| # ", and" / "; and" → and | |
| for word in ("and", "or", "but"): | |
| if re.search(rf"\b{word}\b", raw): | |
| return word | |
| if raw in {";", ","} or raw.endswith(";") or raw.endswith(","): | |
| return "and" # list punctuation → treat as "and" for residuals | |
| return None | |
| ############################################################################### | |
| ############################################################################### | |
| def rewrite_residual_list_comma(sent, coordinator="and"): | |
| """ | |
| Last list-separator comma → same conjunction as the split sign, | |
| so the next pass sees CMN1/CMV1/CCV more reliably (not SSMN). | |
| Skips comma before a bare number (e.g. Smith, 43). | |
| """ | |
| if not sent or not coordinator: | |
| return sent | |
| coord = coordinator.strip().lower() | |
| if coord not in {"and", "or", "but"}: | |
| return sent | |
| s = sent.strip() | |
| m = re.search(r",\s+(?!\d\b)([^,]+?)(\s*[.!?])?\s*$", s) | |
| if not m: | |
| return s | |
| head = s[: m.start()].rstrip() | |
| tail = m.group(1).strip() | |
| punct = (m.group(2) or "").strip() or "." | |
| out = f"{head} {coord} {tail}{punct}" | |
| out = re.sub(r"\s+", " ", out).strip() | |
| return out | |
| ############################################################################### | |
| ############################################################################### | |
| def _singular_present_form(lemma: str) -> str: | |
| """Map verb lemma → 3rd person singular present.""" | |
| lemma = (lemma or "").lower() | |
| irregular = { | |
| "be": "is", | |
| "have": "has", | |
| "do": "does", | |
| "go": "goes", | |
| "say": "says", | |
| } | |
| if lemma in irregular: | |
| return irregular[lemma] | |
| if lemma.endswith(("s", "sh", "ch", "x", "z", "o")): | |
| return lemma + "es" | |
| if len(lemma) > 1 and lemma.endswith("y") and lemma[-2] not in "aeiou": | |
| return lemma[:-1] + "ies" | |
| return lemma + "s" | |
| def subject_is_singular(subj_token) -> bool: | |
| """True if the nominal subject should take singular agreement.""" | |
| # Coordinated subjects → plural | |
| if any(c.dep_ == "cc" for c in subj_token.subtree): | |
| return False | |
| # Explicit plural morph / tags | |
| num = subj_token.morph.get("Number") | |
| if num == ["Plur"]: | |
| return False | |
| if num == ["Sing"]: | |
| return True | |
| if subj_token.tag_ in ("NNS", "NNPS", "PRP") and subj_token.lower_ in { | |
| "we", "they", "you", "i" | |
| }: | |
| # I/you are special; treat as not 3sg | |
| return False | |
| if subj_token.tag_ in ("NNS", "NNPS"): | |
| return False | |
| if subj_token.lower_ in {"i", "you", "we", "they"}: | |
| return False | |
| # he/she/it / singular NN/NNP / this/that | |
| if subj_token.lower_ in {"he", "she", "it", "this", "that"}: | |
| return True | |
| if subj_token.tag_ in ("NN", "NNP", "DT", "PRP$"): | |
| return True | |
| # fallback: whole subject span text | |
| text = " ".join(t.text for t in subj_token.subtree).lower() | |
| if " and " in text: | |
| return False | |
| return True | |
| def fix_subject_verb_agreement(sent: str) -> str: | |
| """ | |
| After CMN1 splits a plural subject, repair the finite verb / aux. | |
| The man live … → The man lives … | |
| The man are going … → The man is going … | |
| """ | |
| if not sent or not sent.strip(): | |
| return sent | |
| sent = sent.strip() | |
| doc = nlp(sent) | |
| if not doc: | |
| return sent | |
| # --- subject: first nsubj, else first noun --- | |
| subj = next((t for t in doc if t.dep_ in ("nsubj", "nsubjpass")), None) | |
| if subj is None: | |
| subj = next((t for t in doc if t.tag_ in ("NN", "NNP", "PRP")), None) | |
| if subj is None: | |
| return sent | |
| # Coordinated subject still plural | |
| if any(t.dep_ == "cc" for t in subj.subtree): | |
| return sent | |
| if subj.lower_ in {"i", "you", "we", "they"}: | |
| return sent | |
| if subj.tag_ in ("NNS", "NNPS"): | |
| return sent | |
| if subj.tag_ not in ("NN", "NNP", "PRP"): | |
| return sent | |
| def to_3sg(word: str) -> str: | |
| low = word.lower() | |
| irregular = { | |
| "are": "is", | |
| "were": "was", | |
| "have": "has", | |
| "do": "does", | |
| "go": "goes", | |
| "live": "lives", | |
| } | |
| if low in irregular: | |
| new = irregular[low] | |
| elif low.endswith(("s", "sh", "ch", "x", "z", "o")): | |
| new = low + "es" | |
| elif low.endswith("y") and len(low) > 1 and low[-2] not in "aeiou": | |
| new = low[:-1] + "ies" | |
| else: | |
| new = low + "s" | |
| if word[:1].isupper(): | |
| new = new[0].upper() + new[1:] | |
| return new | |
| # Prefer agreeing AUX (are going, have gone, …) | |
| target = None | |
| for t in doc: | |
| if t.dep_ in ("aux", "auxpass") and t.lower_ in { | |
| "are", "were", "have", "do", "am" | |
| }: | |
| target = t | |
| break | |
| if target is None: | |
| for t in doc: | |
| if t.tag_ in ("VBP", "VB") and t.lower_ not in {"to"}: | |
| target = t | |
| break | |
| if target is None: | |
| return sent | |
| if target.lower_ in {"is", "was", "has", "does"}: | |
| return sent | |
| new = to_3sg(target.text) | |
| return sent[: target.idx] + new + sent[target.idx + len(target.text) :] | |
| ############################################################################### | |
| ############################################################################### | |
| def CompoundSimplify(xml_sent): | |
| """ | |
| Reconstruct two sentences from the compound XML structure. | |
| sent1 = BEFORE + BEFORESIGN + AFTER | |
| sent2 = BEFORE + AFTERSIGN + AFTER | |
| The coordinator (IN_COMPOUND_CONSTITUENT) is discarded. | |
| Recovery: if AFTERSIGN is empty but IN_COMPOUND contains tokens | |
| after the coordinator, treat those tokens as AFTERSIGN. | |
| """ | |
| try: | |
| def get_text(elem): | |
| if elem is None: | |
| return "" | |
| parts = [] | |
| for w in elem.findall("W"): | |
| if w.text: | |
| t = str(w.text).strip() | |
| t = re.sub(r"[\[\]]+", "", t) | |
| if t: | |
| parts.append(t) | |
| return " ".join(parts).strip() | |
| def get_tokens(elem): | |
| if elem is None: | |
| return [] | |
| parts = [] | |
| for w in elem.findall("W"): | |
| if w.text: | |
| t = str(w.text).strip() | |
| t = re.sub(r"[\[\]]+", "", t) | |
| if t: | |
| parts.append(t) | |
| return parts | |
| before_elem = xml_sent.find(".//BEFORE_COMPOUND_CONSTITUENT") | |
| beforesign_elem = xml_sent.find(".//IN_COMPOUND_CONSTITUENT_BEFORESIGN") | |
| sign_elem = xml_sent.find(".//IN_COMPOUND_CONSTITUENT") | |
| aftersign_elem = xml_sent.find(".//IN_COMPOUND_CONSTITUENT_AFTERSIGN") | |
| after_elem = xml_sent.find(".//AFTER_COMPOUND_CONSTITUENT") | |
| before_text = get_text(before_elem) | |
| beforesign_text = get_text(beforesign_elem) | |
| aftersign_text = get_text(aftersign_elem) | |
| after_text = get_text(after_elem) | |
| # ----- recovery: second clause folded into IN_COMPOUND ----- | |
| if not aftersign_text and sign_elem is not None: | |
| sign_toks = get_tokens(sign_elem) | |
| coord = {",", ";", ":", "and", "but", "or", "nor"} | |
| i = 0 | |
| while i < len(sign_toks) and sign_toks[i].lower() in coord: | |
| i += 1 | |
| if i < len(sign_toks): | |
| aftersign_text = " ".join(sign_toks[i:]).strip() | |
| # ----------------------------------------------------------- | |
| sent1 = " ".join( | |
| x for x in [before_text, beforesign_text, after_text] if x | |
| ).strip() | |
| sent2 = " ".join( | |
| x for x in [before_text, aftersign_text, after_text] if x | |
| ).strip() | |
| def tidy(s): | |
| if not s: | |
| return "" | |
| s = str(s) | |
| s = re.sub(r"\s+", " ", s).strip() | |
| s = re.sub(r"\s+([.,!?;:])", r"\1", s) | |
| s = re.sub(r"([.,!?;:])\s+", r"\1 ", s) | |
| # decimals: 8 . 2 → 8.2 | |
| s = re.sub(r"(\d)\s*\.\s*(\d)", r"\1.\2", s) | |
| # list debris only at the end / before final period | |
| s = re.sub(r"[,;:]+\s*\.", ".", s) | |
| s = re.sub(r"[,;:]+\s*$", "", s) | |
| s = s.strip() | |
| if s and not s.endswith((".", "!", "?")): | |
| s += "." | |
| if s: | |
| s = s[0].upper() + s[1:] | |
| return s | |
| sent1 = tidy(sent1) | |
| sent2 = tidy(sent2) | |
| result = [] | |
| if sent1: | |
| result.append(sent1) | |
| if sent2 and sent2 != sent1: | |
| result.append(sent2) | |
| print(f"CompoundSimplify produced {len(result)} sentences: {result}") | |
| return result | |
| except Exception as e: | |
| print(f"CompoundSimplify error: {e}") | |
| return [] | |
| ############################################################################### | |
| ############################################################################### | |
| def create_xml_sent(tokens, pos_tags, lemmas, predictions, sign_tag): | |
| xml_sent = ET.Element("SENT") | |
| print( | |
| "tokens:", | |
| tokens, | |
| "\npos_tags:", | |
| pos_tags, | |
| "\nlemmas:", | |
| lemmas, | |
| "\npredictions:", | |
| predictions, | |
| "\nsign_tag:", | |
| sign_tag, | |
| ) | |
| before_complex_constituent = None | |
| in_complex_constituent_beforesign = None | |
| in_complex_constituent = None | |
| in_complex_constituent_aftersign = None | |
| after_complex_constituent = None | |
| if sign_tag in "M:Y_SSCCV": | |
| before_complex_constituent = ET.SubElement( | |
| xml_sent, "BEFORE_COMPLEX_CONSTITUENT" | |
| ) | |
| in_complex_constituent_beforesign = ET.SubElement( | |
| xml_sent, "IN_COMPLEX_CONSTITUENT_BEFORESIGN" | |
| ) | |
| in_complex_constituent = ET.SubElement(xml_sent, "IN_COMPLEX_CONSTITUENT") | |
| in_complex_constituent_aftersign = ET.SubElement( | |
| xml_sent, "IN_COMPLEX_CONSTITUENT_AFTERSIGN" | |
| ) | |
| after_complex_constituent = ET.SubElement(xml_sent, "AFTER_COMPLEX_CONSTITUENT") | |
| elif sign_tag in ["M:Y_SSMN"]: # SSMA later if you want ages | |
| xml_sent.set("TYPE", "COMPLEX_NP") # or "APPOSITIVE" | |
| before_complex_constituent = ET.SubElement( | |
| xml_sent, "BEFORE_COMPLEX_CONSTITUENT" | |
| ) | |
| in_complex_constituent_beforesign = ET.SubElement( | |
| xml_sent, "IN_COMPLEX_CONSTITUENT_BEFORESIGN" | |
| ) | |
| in_complex_constituent = ET.SubElement( | |
| xml_sent, "IN_COMPLEX_CONSTITUENT" | |
| ) | |
| in_complex_constituent_aftersign = ET.SubElement( | |
| xml_sent, "IN_COMPLEX_CONSTITUENT_AFTERSIGN" | |
| ) | |
| after_complex_constituent = ET.SubElement( | |
| xml_sent, "AFTER_COMPLEX_CONSTITUENT" | |
| ) | |
| elif sign_tag in ["M:Y_CCV", "M:Y_CMV1", "M:Y_CMN1", "M:Y_CLN"]: | |
| xml_sent.set("TYPE", "COMPOUND") | |
| before_complex_constituent = ET.SubElement( | |
| xml_sent, "BEFORE_COMPOUND_CONSTITUENT" | |
| ) | |
| in_complex_constituent_beforesign = ET.SubElement( | |
| xml_sent, "IN_COMPOUND_CONSTITUENT_BEFORESIGN" | |
| ) | |
| in_complex_constituent = ET.SubElement(xml_sent, "IN_COMPOUND_CONSTITUENT") | |
| in_complex_constituent_aftersign = ET.SubElement( | |
| xml_sent, "IN_COMPOUND_CONSTITUENT_AFTERSIGN" | |
| ) | |
| after_complex_constituent = ET.SubElement( | |
| xml_sent, "AFTER_COMPOUND_CONSTITUENT" | |
| ) | |
| core_prediction = predictions[0] | |
| core_prediction = re.sub("IN_", "", core_prediction) | |
| core_prediction = re.sub("BEFORE_", "", core_prediction) | |
| core_prediction = re.sub("AFTER_", "", core_prediction) | |
| core_prediction = re.sub("_AFTERSIGN", "", core_prediction) | |
| core_prediction = re.sub("_BEFORESIGN", "", core_prediction) | |
| xml_sent.attrib["TYPE"] = core_prediction | |
| x = 0 | |
| while x < len(predictions): | |
| if "_BEFORESIGN" in predictions[x]: | |
| word = ET.SubElement(in_complex_constituent_beforesign, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| elif "_AFTERSIGN" in predictions[x]: | |
| if re.match( | |
| "(and|but|or|that|what|when|where|which|while|who)", | |
| tokens[x], | |
| re.IGNORECASE, | |
| ): | |
| if x > 0 and tokens[x - 1] in [",", ":", ";"]: | |
| word = ET.SubElement(in_complex_constituent, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| else: | |
| word = ET.SubElement(in_complex_constituent_aftersign, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| else: | |
| word = ET.SubElement(in_complex_constituent_aftersign, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| elif ( | |
| "NOT_NOUN_COORDINATOR" in predictions[x] | |
| or "NOT_NP_COORDINATOR" in predictions[x] | |
| or "NOT_CLAUSE_COORDINATOR" in predictions[x] | |
| or "NOT_VP_COORDINATOR" in predictions[x] | |
| ): | |
| word = ET.SubElement(in_complex_constituent_beforesign, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| elif "IN_" in predictions[x]: | |
| word = ET.SubElement(in_complex_constituent, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| elif "BEFORE_" in predictions[x]: | |
| word = ET.SubElement(before_complex_constituent, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| elif "AFTER_" in predictions[x]: | |
| word = ET.SubElement(after_complex_constituent, "W") | |
| word.text = tokens[x] | |
| word.attrib["POS"] = pos_tags[x] | |
| word.attrib["LEMMA"] = lemmas[x] | |
| x += 1 | |
| # Print the XML (as you requested) | |
| print("BUILT XML SENTENCE:\n" + ET.tostring(xml_sent).decode("utf-8")) | |
| return xml_sent | |
| ############################################################################# | |
| ############################################################################# | |
| def get_COMPLEX_NP_type(xml_sent): | |
| complex_NP_type = "UNKNOWN" | |
| final_icca_pos_tag = "UNKNOWN" | |
| first_icca_pos_tag = "UNKNOWN" | |
| final_iccb_pos_tag = "UNKNOWN" | |
| punct = re.compile(r"^[\,\:\;]$") | |
| VERB_TAGS = ["VB", "VBD", "VBG", "VBN", "VBZ", "VBP", "MD"] | |
| NOMINAL_TAGS = ["NNP", "NNPS", "NN", "NNS", "DT", "CD", "PRP", "PRP$"] | |
| for el in xml_sent: | |
| if el.tag == "IN_COMPLEX_CONSTITUENT_AFTERSIGN": | |
| try: | |
| # first *content* token, not a leading comma | |
| for w in el: | |
| if w.text and not punct.match(w.text.strip()): | |
| first_icca_pos_tag = w.attrib.get("POS", "UNKNOWN") | |
| break | |
| # last *content* token, not a trailing comma | |
| for w in reversed(list(el)): | |
| if w.text and not punct.match(w.text.strip()): | |
| final_icca_pos_tag = w.attrib.get("POS", "UNKNOWN") | |
| break | |
| except Exception: | |
| pass | |
| elif el.tag == "IN_COMPLEX_CONSTITUENT_BEFORESIGN": | |
| try: | |
| # last *content* token, not a trailing comma | |
| for w in reversed(list(el)): | |
| if w.text and not punct.match(w.text.strip()): | |
| final_iccb_pos_tag = w.attrib.get("POS", "UNKNOWN") | |
| break | |
| except Exception: | |
| pass | |
| if final_iccb_pos_tag in ["IN"]: | |
| complex_NP_type = "PREPOSITION_OBJECT_RELATIVISED" | |
| elif first_icca_pos_tag in NOMINAL_TAGS and final_icca_pos_tag in VERB_TAGS: | |
| complex_NP_type = "VERB_OBJECT_RELATIVISED" | |
| elif first_icca_pos_tag in VERB_TAGS: | |
| complex_NP_type = "SUBJECT_RELATIVISED" | |
| return complex_NP_type | |
| ############################################################################# | |
| ############################################################################# | |
| def simplified_sent_xml_to_text(xml): | |
| parts = [] | |
| for el in xml: | |
| if el.tag == "W": | |
| if el.text: | |
| parts.append(el.text.strip()) | |
| else: | |
| for sub_el in el: | |
| if sub_el.text: | |
| parts.append(sub_el.text.strip()) | |
| # Join the tokens with single spaces | |
| text = " ".join(parts).strip() | |
| # Clean up any leftover multiple spaces | |
| text = re.sub(r"\s+", " ", text) | |
| # Ensure the sentence ends with a full stop | |
| if text and not text.endswith((".", "!", "?")): | |
| text += "." | |
| # Capitalise only the first character, leave everything else unchanged | |
| if text: | |
| text = text[0].upper() + text[1:] | |
| return text | |
| ############################################################################# | |
| ############################################################################### | |
| def clean_bpe_text(text): | |
| """Remove BPE fragments, labels, brackets, and normalize whitespace.""" | |
| text = re.sub(r"##", "", text) # remove subword markers | |
| text = re.sub(r"\s*\[[^\]]+\]\s*\(M:Y_[^\)]+\)", "", text) # remove labels | |
| text = re.sub(r"\s*\[[^\]]+\]", "", text) # remove brackets | |
| text = re.sub(r"\s*\(M:Y_[^\)]+\)", "", text) | |
| text = re.sub(r"\s*\(M:N_[^\)]+\)", "", text) | |
| text = re.sub(r"M Y _[^\s]+", "", text) | |
| text = re.sub(r"\s+", " ", text).strip() | |
| return text | |
| ############################################################################### | |
| ############################################################################### | |
| def clean_output(text): | |
| if not text: | |
| return "" | |
| # brackets and WordPiece fragments | |
| text = re.sub(r'[\[\]]+', '', text) | |
| text = re.sub(r'\s*##\s*', '', text) | |
| text = re.sub(r'#', '', text) | |
| text = re.sub(r'\s+', ' ', text).strip() | |
| # normal spacing around punctuation | |
| text = re.sub(r'\s+([.,!?;:])', r'\1', text) | |
| text = re.sub(r'([.,!?;:])\s+', r'\1 ', text) | |
| # --- fix the trailing ",." pattern --- | |
| text = re.sub(r',+\s*\.', '.', text) # ",." or ", ." → "." | |
| text = re.sub(r',+\s*$', '', text) # trailing commas at end of string | |
| text = re.sub(r'\.+$', '.', text) # collapse multiple periods | |
| text = text.strip() | |
| if text: | |
| text = text[0].upper() + text[1:] | |
| if not text.endswith(('.', '!', '?')): | |
| text += '.' | |
| return text.strip() | |
| ############################################################################### | |
| ############################################################################### | |
| def drop_bracket_tokens(tokens, pos, lemmas, preds): | |
| """Remove literal [ and ] that the tokenizer sometimes emits""" | |
| kept_tokens, kept_pos, kept_lemmas, kept_preds = [], [], [], [] | |
| for t, p, l, pr in zip(tokens, pos, lemmas, preds): | |
| if t in ('[', ']'): | |
| continue | |
| kept_tokens.append(t) | |
| kept_pos.append(p) | |
| kept_lemmas.append(l) | |
| kept_preds.append(pr) | |
| return kept_tokens, kept_pos, kept_lemmas, kept_preds | |
| ############################################################################### | |
| ############################################################################### | |
| def build_single_sign_bracketed(tagged_list, mode="ccv"): | |
| """ | |
| mode = "ccv" → bracket only the rightmost CCV sign | |
| mode = "cmv1" → bracket only the rightmost CMV1 sign | |
| mode = "cmn1" → bracket only the rightmost CMN1 sign | |
| mode = "cln" → bracket only the rightmost CLN sign | |
| mode = "ssccv" → bracket one relative SSCCV trigger: | |
| - nested relatives (2+ opens before next ESCCV): innermost | |
| (rightmost preferred trigger before that ESCCV) | |
| - coordinated relatives (CCV between opens): leftmost | |
| - otherwise: leftmost relative trigger | |
| - fallback: leftmost SSCCV/ESCCV (e.g. bare "that") | |
| mode = "ssmn" → bracket only the leftmost SSMN sign | |
| """ | |
| def surface_of(item): | |
| if item.startswith("<M:Y_") and ">" in item: | |
| start = item.find(">") + 1 | |
| end = item.rfind("<") | |
| if end > start: | |
| return item[start:end] | |
| return item | |
| ccv_indices = [] | |
| cmv1_indices = [] | |
| cmn1_indices = [] | |
| cln_indices = [] | |
| ssccv_indices = [] | |
| esccv_indices = [] | |
| ssmn_indices = [] | |
| for i, item in enumerate(tagged_list): | |
| s = str(item) | |
| if "<M:Y_CCV>" in s or "M:Y_CCV" in s: | |
| ccv_indices.append(i) | |
| if "<M:Y_CMV1>" in s or "M:Y_CMV1" in s: | |
| cmv1_indices.append(i) | |
| if "<M:Y_CMN1>" in s or "M:Y_CMN1" in s: | |
| cmn1_indices.append(i) | |
| if "<M:Y_CLN>" in s or "M:Y_CLN" in s: | |
| cln_indices.append(i) | |
| if "M:Y_SSCCV" in s: | |
| ssccv_indices.append(i) | |
| if "M:Y_ESCCV" in s: | |
| esccv_indices.append(i) | |
| if i not in ssccv_indices: | |
| ssccv_indices.append(i) | |
| if "M:Y_SSMN" in s: | |
| ssmn_indices.append(i) | |
| target = set() | |
| if mode == "ccv" and ccv_indices: | |
| target.add(ccv_indices[-1]) | |
| elif mode == "cmv1" and cmv1_indices: | |
| target.add(cmv1_indices[-1]) | |
| elif mode == "cmn1" and cmn1_indices: | |
| target.add(cmn1_indices[-1]) | |
| elif mode == "cln" and cln_indices: | |
| target.add(cln_indices[-1]) | |
| elif mode == "ssmn" and ssmn_indices: | |
| target.add(ssmn_indices[0]) # leftmost appositive boundary | |
| elif mode == "ssccv" and ssccv_indices: | |
| relative_pronouns = {"who", "which", "whom", "whose"} | |
| preferred = [] | |
| for i in ssccv_indices: | |
| surface = surface_of(tagged_list[i]).strip().lower() | |
| # Direct relative pronoun as the tagged sign | |
| if surface in relative_pronouns: | |
| preferred.append(i) | |
| continue | |
| # Punctuation that introduces a relative (e.g. "," before "who") | |
| if surface in {",", ";", ":"} and i + 1 < len(tagged_list): | |
| nxt = surface_of(tagged_list[i + 1]).strip().lower() | |
| if nxt in relative_pronouns or nxt == "that": | |
| preferred.append(i) | |
| continue | |
| if preferred: | |
| nested = False | |
| coordinated = False | |
| if esccv_indices: | |
| first_es = min(esccv_indices) | |
| opens_before_close = [i for i in preferred if i < first_es] | |
| if len(opens_before_close) >= 2: | |
| first_open = opens_before_close[0] | |
| last_open = opens_before_close[-1] | |
| # CCV between relative openers → coordinated relatives, not nesting | |
| ccv_between = any( | |
| first_open < j < last_open for j in ccv_indices | |
| ) | |
| if ccv_between: | |
| coordinated = True | |
| target.add(opens_before_close[0]) # leftmost / whole relative | |
| else: | |
| nested = True | |
| target.add(opens_before_close[-1]) # innermost | |
| if not nested and not coordinated: | |
| target.add(preferred[0]) # single relative or siblings | |
| else: | |
| pure_ssccv = [ | |
| i for i, item in enumerate(tagged_list) | |
| if "M:Y_SSCCV" in str(item) | |
| ] | |
| target.add((pure_ssccv or ssccv_indices)[0]) | |
| parts = [] | |
| for i, item in enumerate(tagged_list): | |
| if item.startswith("<M:Y_") and item.endswith(">"): | |
| start = item.find(">") + 1 | |
| end = item.rfind("<") | |
| surface = item[start:end] if end > start else item | |
| if i in target: | |
| parts.append(f"[{surface}]") | |
| else: | |
| parts.append(surface) | |
| else: | |
| parts.append(item) | |
| text = " ".join(parts) | |
| # spacing | |
| text = re.sub(r"\s+\[([,;:])", r"[\1", text) | |
| text = re.sub(r"(?<!\[)\[([A-Za-z])", r" [\1", text) | |
| text = re.sub(r"\[\s+", "[", text) | |
| text = re.sub(r"\s+\]", "]", text) | |
| text = re.sub(r"\s+", " ", text).strip() | |
| # merge [, who] / [, which] style | |
| text = re.sub( | |
| r"\[([,;:])\]\s+(who|which|that|whom|whose)\b", | |
| r"[\1 \2]", | |
| text, | |
| flags=re.I, | |
| ) | |
| return text | |
| ############################################################################### | |
| ############################################################################### | |
| def process_text(input_text): | |
| print("=== ITERATIVE SIMPLIFICATION (priority + clean tokens) ===") | |
| try: | |
| MAX_ITERS = 40 | |
| # ==================== PREPROCESSING ==================== | |
| input_text = re.sub(r"supposingthat", "supposing that", input_text) | |
| input_text = re.sub(r'"', "''", input_text) | |
| input_text = re.sub(r"\.\s+([a-z])", ".\n\\g<1>", input_text) | |
| input_text = re.sub(r"mainly f or", "mainly for", input_text) | |
| input_text = re.sub(r"sothat", "so that", input_text) | |
| input_text = re.sub(r"in alabama", "in Alabama", input_text) | |
| input_text = re.sub( | |
| r"LEISURE DESIGN IS IMPORTANT WHY is", | |
| "LEISURE DESIGN IS IMPORTANT\n\nWhy is", | |
| input_text, | |
| ) | |
| input_text = re.sub(r"WHEN", "When", input_text) | |
| input_text = re.sub(r"WHAT'S", "What's", input_text) | |
| input_text = re.sub(r"(\s+)an'", " and", input_text) | |
| input_text = re.sub(r"An'", "And", input_text) | |
| # ======================================================= | |
| raw_paragraphs = re.split(r"\n\s*\n", input_text) | |
| output_paragraphs = [] | |
| for para in raw_paragraphs: | |
| para = para.strip() | |
| if not para: | |
| continue | |
| para = re.sub(r"\s*\n\s*", " ", para) | |
| para = re.sub(r"\s+", " ", para).strip() | |
| sents = split_into_sentences(para) | |
| if not sents: | |
| continue | |
| print(f"Paragraph → {len(sents)} sentence(s):") | |
| for i, s in enumerate(sents, 1): | |
| print(f" [{i}] {s}") | |
| para_simplified = [] | |
| for original_sent in sents: | |
| working_set = deque([original_sent]) | |
| seen = set() | |
| sentence_final = [] | |
| iters = 0 | |
| while working_set: | |
| iters += 1 | |
| if iters > MAX_ITERS: | |
| print( | |
| f"ABORT: exceeded {MAX_ITERS} iterations; " | |
| f"draining {len(working_set)} item(s) to final" | |
| ) | |
| while working_set: | |
| sentence_final.append( | |
| clean_output(working_set.popleft()) | |
| ) | |
| break | |
| current = working_set.popleft().strip() | |
| if not current: | |
| continue | |
| current = clean_output(current) | |
| if not current: | |
| continue | |
| key = re.sub(r"\s+", " ", current.lower()) | |
| if key in seen: | |
| print(f"SKIP duplicate: {current[:80]}...") | |
| sentence_final.append(current) | |
| continue | |
| seen.add(key) | |
| print( | |
| f"\n--- iter {iters} | queue={len(working_set)} | " | |
| f"{current[:100]}..." | |
| ) | |
| tagged_list = tag_all_signs_in_sent(current) | |
| has_ssccv = any( | |
| any(x in t for x in ["M:Y_SSCCV", "M:Y_ESCCV"]) | |
| for t in tagged_list | |
| ) | |
| has_ssmn = any( | |
| any(x in t for x in ["M:Y_SSMN", "M:Y_ESMN"]) | |
| for t in tagged_list | |
| ) | |
| has_ccv = any( | |
| "<M:Y_CCV>" in t or "M:Y_CCV" in t for t in tagged_list | |
| ) | |
| has_cmv1 = any( | |
| "<M:Y_CMV1>" in t or "M:Y_CMV1" in t for t in tagged_list | |
| ) | |
| has_cmn1 = any( | |
| "<M:Y_CMN1>" in t or "M:Y_CMN1" in t for t in tagged_list | |
| ) | |
| has_cln = any( | |
| "<M:Y_CLN>" in t or "M:Y_CLN" in t for t in tagged_list | |
| ) | |
| if has_ssccv: | |
| mode = "ssccv" | |
| xml_tag = "M:Y_SSCCV" | |
| simplifier = SSCCVsimplify | |
| span_fn = tag_SSCCV_span | |
| elif has_ssmn: | |
| mode = "ssmn" | |
| xml_tag = "M:Y_SSMN" | |
| simplifier = SSMNsimplify | |
| span_fn = tag_SSMN_span | |
| elif has_ccv: | |
| mode = "ccv" | |
| xml_tag = "M:Y_CCV" | |
| simplifier = CompoundSimplify | |
| span_fn = tag_CCV_span | |
| elif has_cmv1: | |
| mode = "cmv1" | |
| xml_tag = "M:Y_CMV1" | |
| simplifier = CompoundSimplify | |
| span_fn = tag_CMV1_span | |
| elif has_cmn1: | |
| mode = "cmn1" | |
| xml_tag = "M:Y_CMN1" | |
| simplifier = CompoundSimplify | |
| span_fn = tag_CMN1_span | |
| elif has_cln: | |
| mode = "cln" | |
| xml_tag = "M:Y_CLN" | |
| simplifier = CompoundSimplify | |
| span_fn = tag_CLN_span | |
| else: | |
| sentence_final.append(current) | |
| continue | |
| bracketed_version = build_single_sign_bracketed( | |
| tagged_list, mode=mode | |
| ) | |
| print(f"\nProcessing: {current}") | |
| print(f"Bracketed version ({mode}): {bracketed_version}") | |
| cleaned = clean_for_spaCy(bracketed_version) | |
| tokens, pos, lemmas, preds = span_fn(cleaned) | |
| tokens, pos, lemmas, preds = drop_bracket_tokens( | |
| tokens, pos, lemmas, preds | |
| ) | |
| cleaned_tokens, clean_pos, clean_lemmas, cleaned_preds = ( | |
| sign_cleaning(tokens, pos, lemmas, preds) | |
| ) | |
| improved_preds = improve_predicted_tag_sequence( | |
| cleaned_preds | |
| ) | |
| xml_sent = create_xml_sent( | |
| cleaned_tokens, | |
| clean_pos, | |
| clean_lemmas, | |
| improved_preds, | |
| xml_tag, | |
| ) | |
| simps = simplifier(xml_sent) | |
| if simps: | |
| simps = [fix_subject_verb_agreement(s) for s in simps] | |
| # Residual comma → same conjunction (CCV / CMV1 / CMN1 / CLN only) | |
| if ( | |
| simps | |
| and simplifier is CompoundSimplify | |
| and xml_tag | |
| in ("M:Y_CCV", "M:Y_CMV1", "M:Y_CMN1", "M:Y_CLN") | |
| ): | |
| coord = extract_coordinator_from_xml(xml_sent) | |
| if coord: | |
| rewritten = [] | |
| for s in simps: | |
| s2 = rewrite_residual_list_comma(s, coord) | |
| if s2 != s: | |
| print( | |
| f" Residual list rewrite ({coord}): " | |
| f"{s!r} → {s2!r}" | |
| ) | |
| rewritten.append(s2) | |
| simps = rewritten | |
| # Subject–verb agreement after coordinated-NP (CMN1) splits | |
| if simps and xml_tag == "M:Y_CMN1": | |
| simps = [fix_subject_verb_agreement(s) for s in simps] | |
| if not simps: | |
| print( | |
| "Simplifier returned []; forcing final (no re-queue)" | |
| ) | |
| current = fix_subject_verb_agreement(current) | |
| sentence_final.append(current) | |
| continue | |
| queued_any = False | |
| for s in simps: | |
| plain_s = clean_output(s) | |
| plain_s = fix_subject_verb_agreement(plain_s) | |
| if not plain_s: | |
| continue | |
| plain_key = re.sub(r"\s+", " ", plain_s.lower()) | |
| if plain_key == key: | |
| print( | |
| f" No progress (same as input): " | |
| f"{plain_s[:80]}..." | |
| ) | |
| sentence_final.append(plain_s) | |
| continue | |
| if plain_key in seen: | |
| print( | |
| f" Skip already-seen result: " | |
| f"{plain_s[:80]}..." | |
| ) | |
| sentence_final.append(plain_s) | |
| continue | |
| working_set.append(plain_s) | |
| queued_any = True | |
| print(f" → Queued: {plain_s}") | |
| if not queued_any: | |
| sentence_final.append(current) | |
| for s in order_sentences_for_discourse(sentence_final): | |
| s = clean_output(s) | |
| if s: | |
| para_simplified.append(s) | |
| if para_simplified: | |
| para_simplified = order_sentences_for_discourse(para_simplified) | |
| output_paragraphs.append(" ".join(para_simplified)) | |
| final_output = "\n\n".join(output_paragraphs) | |
| print(f"\n=== FINAL OUTPUT ===\n{final_output}") | |
| return final_output | |
| except Exception as e: | |
| import traceback | |
| print(traceback.format_exc()) | |
| return f"ERROR: {str(e)}" | |
| ########################################################################### | |
| # Main processing function (encapsulates the pipeline) | |
| # ================== GRADIO INTERFACE =================== | |
| print(" All seven models loaded successfully!") | |
| print("Creating Gradio interface...") | |
| custom_css = """ | |
| /* Slightly larger base font */ | |
| .gradio-container, .gradio-container * { | |
| font-size: 16px !important; | |
| } | |
| /* Input = original text → bright pink */ | |
| .input-text textarea { | |
| font-size: 17px !important; | |
| color: #ffd0d0 !important; /* DeepPink */ | |
| font-weight: 500 !important; | |
| line-height: 1.45 !important; | |
| } | |
| /* Output = simplified text → bright green */ | |
| .output-text textarea { | |
| font-size: 17px !important; | |
| color: #d0ffd0 !important; /* bright green */ | |
| font-weight: 500 !important; | |
| line-height: 1.45 !important; | |
| } | |
| /* Labels under the boxes */ | |
| .input-text label, | |
| .output-text label { | |
| font-size: 15px !important; | |
| font-weight: 600 !important; | |
| } | |
| .input-text label { | |
| color: #ffd0d0 !important; | |
| } | |
| .output-text label { | |
| color: #d0ffd0 !important; | |
| } | |
| """ | |
| with gr.Blocks(title="Syntactic Sentence Simplifier") as demo: | |
| gr.Markdown("# Syntactic Sentence Simplifier") | |
| gr.Markdown( | |
| """ | |
| This demo simplifies sentences containing compound constituents: | |
| * *clauses* | |
| * *verb phrases* | |
| * *noun phrases* | |
| * *nouns* | |
| and complex noun phrases modified by: | |
| * *nominally bound non-restrictive finite relative clauses (subject relativised and verb/preposition object relativised)* | |
| * *nominally bound non-restrictive non-finite nominal clauses* | |
| Paste text, or click examples to **add** them into the box below | |
| (you can edit the text afterwards). | |
| """ | |
| ) | |
| # 1) Complex input | |
| input_text = gr.Textbox( | |
| lines=12, | |
| label="Original text", | |
| placeholder="Paste your text here, or add examples below...", | |
| elem_classes=["input-text"], | |
| ) | |
| # 2) Bridge button (directly under input) | |
| btn = gr.Button("Process & Simplify", variant="primary") | |
| # 3) Simple output | |
| output_text = gr.Textbox( | |
| lines=12, | |
| label="Simplified text", | |
| elem_classes=["output-text"], | |
| ) | |
| btn.click(fn=process_text, inputs=input_text, outputs=output_text) | |
| # Examples / clear (under the whole stack, or under input if you prefer) | |
| EXAMPLE_SENTENCES = [ | |
| # "John kicked the ball and walked to Leeds.", | |
| # "John bought two apples and three oranges.", | |
| "John left early and Mary stayed late.", | |
| "John, who lives in London, went home.", | |
| # "Peter, who Sam likes, went home.", | |
| # "Fred bought a computer and a printer and switched them on.", | |
| # "Fred bought a computer and a printer, and Mary bought a PS5.", | |
| # "Mary, who lives in London, went home, and Peter stayed.", | |
| "John, who kicked the ball and walked away, went home.", | |
| # "John, who bought a computer and a printer, went home.", | |
| # "John, who Steven tolerated and who mary despised, went home.", | |
| # "John, who lives in Shifnal, which is a town in the Black Country, went home.", | |
| # "Mary, who lives in London, kicked the ball and walked to Leeds, and John stayed home.", | |
| # "Jackie, who lives in Manchester, bought a computer and a monitor, and Fred bought bread.", | |
| # "Count Binface bought two apples and three pears and ate them, and Nigel left.", | |
| # "Count Binface, who bought two apples and three pears and ate them, went home.", | |
| # "Hadric Wode, who bought a mace and a shield and used them, left early, and Fenric, who was tired, stayed home.", | |
| # "John is one candidate, Mary ignores signals, and Bill eats grass.", | |
| # "John went to Essex, Bill set out for Sussex, and Mary returned from Halifax.", | |
| # "Joshua told me that John, who lives in New York, is going to London, Jack loves Norwich, and Mary, who is extremely confident, adores Milton Keynes, just as Fred predicted.", | |
| # "John kicked the ball and walked to Leeds, and Peter stayed home.", | |
| # "John is one test, Mary, who lives in London, loves exams, and Bill eats bread.", | |
| # "John went to Wolverhampton, Bill set out for Coventry, and Mary returned from Birmingham.", | |
| # "John, who lives in London, is going to Wolverhampton, and Mary, who is a bit nervous, adores Birmingham.", | |
| # "John kicked the ball and walked to Leeds. Mary bought a laptop and a mousemat.", | |
| # "I love France.", | |
| # "John met the dog, the cat, and the tortoise.", | |
| # "The Environment Agency said record winter rainfall had helped to replenish reservoirs and groundwater, but reservoir storage has now fallen to 7.4% below the average for this time of year.", | |
| "The man and his dog, who live in Bristol, the English city, went home and Mary ran a marathon and smoked cigars and cigarettes.", | |
| "The man and the woman, who live in Skegness, are going home.", | |
| ] | |
| def append_example(current_text, example): | |
| example = (example or "").strip() | |
| if not example: | |
| return current_text or "" | |
| current = (current_text or "").strip() | |
| if not current: | |
| return example | |
| return current + "\n\n" + example | |
| gr.Markdown("### Add an example to the box above (click to append; then edit freely)") | |
| clear_btn = gr.Button("Clear input", size="sm") | |
| clear_btn.click(fn=lambda: "", outputs=input_text) | |
| for ex in EXAMPLE_SENTENCES: | |
| label = ex if len(ex) <= 100 else ex[:97] + "..." | |
| ex_btn = gr.Button(label, size="sm") | |
| ex_btn.click( | |
| fn=lambda current, s=ex: append_example(current, s), | |
| inputs=input_text, | |
| outputs=input_text, | |
| ) | |
| print("Gradio interface ready. Starting web server...") | |
| if __name__ == "__main__": | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| debug=True, | |
| share=False, | |
| ) | |
| ''' | |
| test = ( | |
| "Joshua told me that John, who lives in London, is going to Wolverhampton, Jack loves Norwich, and Mary, who is a bit nervous, adores Birmingham, just as Fred predicted." | |
| ) | |
| print(process_text(test)) | |
| ''' | |