Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
semantic-similarity-classification
Languages:
English
Size:
10K - 100K
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - found | |
| - expert-generated | |
| language: | |
| - en | |
| license: | |
| - cc-by-nc-4.0 | |
| multilinguality: | |
| - monolingual | |
| paperswithcode_id: phrase-in-context | |
| pretty_name: 'PiC: Phrase Similarity (PS)' | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - semantic-similarity-classification | |
| # Dataset Card for "PiC: Phrase Similarity" | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** [https://phrase-in-context.github.io/](https://phrase-in-context.github.io/) | |
| - **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context) | |
| - **Paper:** | |
| - **Leaderboard:** | |
| - **Point of Contact:** [Thang Pham](<thangpham@auburn.edu>) | |
| - **Size of downloaded dataset files:** 4.60 MB | |
| - **Size of the generated dataset:** 2.96 MB | |
| - **Total amount of disk used:** 7.56 MB | |
| ### Dataset Summary | |
| PS is a binary classification task with the goal of predicting whether two multi-word noun phrases are semantically similar or not given *the same context* sentence. | |
| This dataset contains ~10K pairs of two phrases along with their contexts used for disambiguation, since two phrases are not enough for semantic comparison. | |
| Our ~10K examples were annotated by linguistic experts on <upwork.com> and verified in two rounds by 1000 Mturkers and 5 linguistic experts. | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| English. | |
| ## Dataset Structure | |
| ### Data Instances | |
| **PS** | |
| * Size of downloaded dataset files: 4.60 MB | |
| * Size of the generated dataset: 2.96 MB | |
| * Total amount of disk used: 7.56 MB | |
| ``` | |
| { | |
| "phrase1": "annual run", | |
| "phrase2": "yearlong performance", | |
| "sentence1": "since 2004, the club has been a sponsor of the annual run for rigby to raise money for off-campus housing safety awareness.", | |
| "sentence2": "since 2004, the club has been a sponsor of the yearlong performance for rigby to raise money for off-campus housing safety awareness.", | |
| "label": 0, | |
| "idx": 0, | |
| } | |
| ``` | |
| ### Data Fields | |
| The data fields are the same among all splits. | |
| * phrase1: a string feature. | |
| * phrase2: a string feature. | |
| * sentence1: a string feature. | |
| * sentence2: a string feature. | |
| * label: a classification label, with negative (0) and positive (1). | |
| * idx: an int32 feature. | |
| ### Data Splits | |
| | name |train |validation|test | | |
| |--------------------|----:|--------:|----:| | |
| |PS |7362| 1052|2102| | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com). | |
| #### Who are the source language producers? | |
| We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification. | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed] | |
| #### Who are the annotators? | |
| 13 linguistic experts from [Upwork.com](https://upwork.com). | |
| ### Personal and Sensitive Information | |
| No annotator identifying details are provided. | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| This dataset is a joint work between Adobe Research and Auburn University. | |
| Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me). | |
| [@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace. | |
| ### Licensing Information | |
| This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) | |
| ### Citation Information | |
| ``` | |
| @article{pham2022PiC, | |
| title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search}, | |
| author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh}, | |
| journal={arXiv preprint arXiv:2207.09068}, | |
| year={2022} | |
| } | |
| ``` |