Document new query metadata columns; fix typos

README.md

@@ -31,7 +31,7 @@ configs:
 
 # POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search
 
-POISS is the firste large-scale dataset for Point-of-Interest (POI) search. Given a natural-language query and
+POISS is the first large-scale dataset for Point-of-Interest (POI) search. Given a natural-language query and
 the coordinate it was issued from, a system ranks POIs by relevance. Relevance here is
 genuinely two-sided: "coffee shops near me" depends on where the user is, while "hotel caribe en
 mérida yucatán" names a specific place hundreds of kilometres away. Models have to weigh
@@ -48,11 +48,11 @@ and [`amazon/poiss-bge-m3-reranker`](https://huggingface.co/amazon/poiss-bge-m3-
 
 ## About
 
-The datasets and models in this repository originate from our paper accepted at the , **Main Conference** of **The 2026 Conference on Empirical Methods in Natural Language Processing**.
+The datasets and models in this repository originate from our paper accepted at the **Main Conference** of **The 2026 Conference on Empirical Methods in Natural Language Processing**.
 
 For more details, please check out the paper [POISS: A Large-Scale Multilingual Dataset for Point-of-Interest Search](POISS_paper.pdf).
 
-✨ **Come visit our poster session in Budapest!** We look forward to meeting you and discussin our work.
+✨ **Come visit our poster session in Budapest!** We look forward to meeting you and discussing our work.
 
 ## What you can build with it
 
@@ -61,10 +61,11 @@ For more details, please check out the paper [POISS: A Large-Scale Multilingual
 - **Reranking with graded relevance.** Up to 20 ranked positives per query let you study
   fine-grained ordering, not just relevant/non-relevant.
 - **Multilingual and cross-lingual behaviour.** English, Spanish, French, Portuguese, German,
-  Italian and Dutch, each with enough test queries to report per-language numbers.
-- **Intent analysis.** Queries split into name-anchored (`Search`, `Detail`) and open-ended
-  (`Recommend`, `Things-to-do`) intents, which behave very differently: open-ended queries stay
-  substantially harder for every system we measured. (Intent labels available soon)
+  Italian and Dutch, each with enough test queries to report per-language numbers. Every row
+  carries `language`, `locale` and `query_country`, so the breakdowns are a `group_by` away.
+- **Intent analysis.** The `intent_group` column splits queries into name-anchored (`Search`,
+  `Detail`) and open-ended (`Recommend`, `Things-to-do`) intents, which behave very differently:
+  open-ended queries stay substantially harder for every system we measured.
 - **Geography versus semantics.** Every query carries its issuing coordinate and every candidate
   its location, so distance can be modelled explicitly, combined with lexical or dense scores,
   or studied as a feature.
@@ -94,6 +95,10 @@ marginally higher.
 | `split` | string | `train` or `test` |
 | `query_text` | string | the query |
 | `user_geolocation` | struct{`lat`,`lng`: float64} | coordinate the query was issued from |
+| `language` | string | ISO 639-1 code, e.g. `it` |
+| `locale` | string | language–country locale, e.g. `it_IT`, `es_US` |
+| `query_country` | string | ISO 3166-1 alpha-2 country, e.g. `IT` |
+| `intent_group` | string | `Search`, `Detail`, `Recommend` or `Things-to-do` |
 | `candidates` | list[string] | ordered Overture GERS identifiers |
 | `candidate_labels` | list[string] | parallel to `candidates`, one label each |
 | `relevant_candidates` | list[string] | the graded-relevant subset (≤ 20), in ranked order |
@@ -124,8 +129,12 @@ from datasets import load_dataset
 
 poiss = load_dataset("amazon/poiss")
 row = poiss["test"][0]
-print(row["query_text"], row["user_geolocation"])
+print(row["query_text"], row["user_geolocation"], row["locale"], row["intent_group"])
 print(row["candidates"][:5], row["candidate_labels"][:5])
+
+# per-language or per-intent slices
+italian = poiss["test"].filter(lambda r: r["language"] == "it")
+open_ended = poiss["test"].filter(lambda r: r["intent_group"] == "Recommend")
 ```
 
 ## Recovering POI content
@@ -180,7 +189,13 @@ against the graded labels. Test split, percentages:
 | Qwen3-Embedding-0.6B fine-tuned | 45.0 | 82.5 | 94.7 | **58.7** | **60.7** |
 
 Fine-tuned per-language R@100 spans 79.8 (NL) to 86.1 (DE); per-intent R@100 is 89.1 on
-`Search` against 77.3 on `Recommend`.
+`Search` against 77.3 on `Recommend`. Both breakdowns can be reproduced from the `language` and
+`intent_group` columns.
+
+The test split is 19.5% US English, 15.0% UK English, 14.4% Canadian English, 8.9% Brazilian
+Portuguese, 8.6% French, 8.2% Spanish (Spain), 7.6% Mexican Spanish, 7.1% Italian, 6.9% German,
+with the remainder spread over Dutch, Irish English, Canadian French, Austrian German and US
+Spanish. By intent it is 49.8% `Search`, 47.8% `Recommend`, 1.9% `Things-to-do`, 0.5% `Detail`.
 
 **Candidate reranking.** A system reorders a per-query pool. The numbers below use the pool from
 the reference setup — the fine-tuned retriever's top-1000 over the full corpus — which can be