--- 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) - **Size of downloaded dataset files:** 25.03 MB - **Size of the generated dataset:** 16.22 MB - **Total amount of disk used:** 41.25 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 ~56K pairs of two phrases along with their contexts used for disambiguation, since two phrases only sometimes are not enough for semantic comparison. Around 28K positive examples were annotated by linguistic experts on while the other 28K negative examples were created by randomly replacing 50% of the phrase tokens in the positive examples. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English. ## Dataset Structure ### Data Instances **PS** * Size of downloaded dataset files: 25.03 MB * Size of the generated dataset: 16.22 MB * Total amount of disk used: 41.25 MB ``` { "phrase1": "greater presence", "phrase2": "msie usage", "sentence1": "The songs on the album feature a greater presence of band member Martin Swope's electronic and tape sound effects than with the band's previous recordings.", "sentence2": "The songs on the album feature a msie usage of band member Martin Swope's electronic and tape sound effects than with the band's previous recordings.", "label": 0, "idx": 1, } ``` ### 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 |39436| 5634|11266| ## 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 and Yoon, David and Bui, Trung and Nguyen, Anh}, journal={arXiv preprint}, year={2022} } ```