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queryner / README.md
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---
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
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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)