File size: 5,219 Bytes
41af597 aaf12af 2570ff5 41af597 dd8e219 41af597 dd8e219 f2f9173 41af597 dd8e219 41af597 f2f9173 41af597 7b16780 41af597 7b16780 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
---
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) |