Update README.md

README.md

@@ -1,74 +1,195 @@
----
-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_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. 
+
+
+- **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]. 
+
+
+- **Repository:** 
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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]
+```
+@article{reddy2022shopping,
+title={Shopping Queries Dataset: A Large-Scale {ESCI} Benchmark for Improving Product Search},
+author={Chandan K. Reddy and Lluís Màrquez and Fran Valero and Nikhil Rao and Hugo Zaragoza and Sambaran Bandyopadhyay and Arnab Biswas and Anlu Xing and Karthik Subbian},
+year={2022},
+eprint={2206.06588},
+archivePrefix={arXiv}
+}
+```
+
+#### 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 [optional]
+
+To appear at LREC-COLING 2024. 
+
+**BibTeX:**
+
+Coming soon
+
+
+
+## Dataset Card Authors
+
+Chester Palen-Michel @cpalenmichel
+
+## Dataset Card Contact
+
+Chester Palen-Michel @cpalenmichel