README.md

---
dataset_info:
  features:
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          '0': O
          '1': B-UoM
          '2': I-UoM
          '3': B-color
          '4': I-color
          '5': B-condition
          '6': I-condition
          '7': B-content
          '8': I-content
          '9': B-core_product_type
          '10': I-core_product_type
          '11': B-creator
          '12': I-creator
          '13': B-department
          '14': I-department
          '15': B-material
          '16': I-material
          '17': B-modifier
          '18': I-modifier
          '19': B-occasion
          '20': I-occasion
          '21': B-origin
          '22': I-origin
          '23': B-price
          '24': I-price
          '25': B-product_name
          '26': I-product_name
          '27': B-product_number
          '28': I-product_number
          '29': B-quantity
          '30': I-quantity
          '31': B-shape
          '32': I-shape
          '33': B-time
          '34': I-time
  splits:
  - name: train
    num_bytes: 553523
    num_examples: 7841
  - name: test
    num_bytes: 70308
    num_examples: 993
  - name: validation
    num_bytes: 61109
    num_examples: 871
  download_size: 242711
  dataset_size: 684940
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
license: cc-by-4.0
task_categories:
- token-classification
language:
- en
pretty_name: QueryNER
size_categories:
- 1K<n<10K
---
# Dataset Card for QueryNER

QueryNER is a sequence labeling dataset for e-commerce query segmentation. 
It has 17 different entity types. QueryNER covers nearly the entire query rather than just certain key aspects that may be covered by other aspect-value extraction systems.


## Dataset Details

### Dataset Description

QueryNER is a manually-annotated dataset and accompanying model for e-commerce query segmentation. Prior work in sequence labeling for e-commerce has largely addressed aspect-value extraction which focuses
on extracting portions of a product title or query for narrowly defined aspects. Our work instead focuses on the goal
of dividing a query into meaningful chunks with broadly applicable types.
QueryNER has 17 different entity types. 

- **Repository:** [QueryNER](https://github.com/bltlab/query-ner)
- **Paper:** Accepted at LREC-COLING 2024, coming soon

- **Curated by:** BLT Lab
- **Language(s) (NLP):** English
- **License:** CC-BY 4.0

### Dataset Sources

QueryNER is annotation on a subsection of Amazon's [ESCI Shopping Queries dataset](https://github.com/amazon-science/esci-data). 

## Uses

QueryNER is intended to be used for segmentation of e-commerce queries in English. 

### Direct Use

QueryNER can be used for research on e-commerce query segmentation. 
It may also be used for e-commerce query segmentation for use in further downstream systems; however, we caution users that while the ontology is broadly applicable, using models trained on only this small public release may have suboptimal performance especially on out of domain data.

### Out-of-Scope Use

Users would likely experience poor segmentation performance on data outside of the e-commerce domain. 
Because the dataset is on the smaller side, additional annotated data on additional data using the QueryNER ontology
may be necessary to get better performance on other datasets.


## Dataset Structure

The dataset includes the query tokens and their tags. 


## Dataset Creation
See paper.

### Curation Rationale

The dataset was created for research and for downstream applications for e-commerce search systems to make use of segmented queries. 


### Source Data

The source data is from the Shopping Queries ESCI dataset. 
[https://github.com/amazon-science/esci-data](https://github.com/amazon-science/esci-data)


#### Data Collection and Processing

See paper


#### Who are the source data producers?

See source data repo and paper. 


### Annotations

#### Annotation process

See paper for details.

#### Who are the annotators?

Annotators were contract workers and were paid a living wage. 

#### Personal and Sensitive Information

The dataset is just user e-commerce queries and should not contain any sensitive information.


## Bias, Risks, and Limitations

The dataset is English only for now. 
Bias may be toward e-commerce queries of the source data. 
There may also be annotator bias since the dataset is annotated by a single annotator for the training set and three annotators and an adjudicator for the development and test sets.


## Citation

To appear at LREC-COLING 2024. 

**BibTeX:**
```
@misc{palenmichel2024queryner,
      title={QueryNER: Segmentation of E-commerce Queries}, 
      author={Chester Palen-Michel and Lizzie Liang and Zhe Wu and Constantine Lignos},
      year={2024},
      eprint={2405.09507},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```


## Dataset Card Authors

Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel)

## Dataset Card Contact

Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel)