{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iiscnlp-at-semeval-2016-task-2-interpretable-1","title":"IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner","arxiv_id":"1605.01194","date":"2016-05-04","proceeding":null,"authors":["Lavanya Sita Tekumalla","Sharmistha"],"abstract":"Interpretable semantic textual similarity (iSTS) task adds a crucial\nexplanatory layer to pairwise sentence similarity. We address various\ncomponents of this task: chunk level semantic alignment along with assignment\nof similarity type and score for aligned chunks with a novel system presented\nin this paper. We propose an algorithm, iMATCH, for the alignment of multiple\nnon-contiguous chunks based on Integer Linear Programming (ILP). Similarity\ntype and score assignment for pairs of chunks is done using a supervised\nmulticlass classification technique based on Random Forrest Classifier. Results\nshow that our algorithm iMATCH has low execution time and outperforms most\nother participating systems in terms of alignment score. Of the three datasets,\nwe are top ranked for answer- students dataset in terms of overall score and\nhave top alignment score for headlines dataset in the gold chunks track.","url_abs":"http://arxiv.org/abs/1605.01194v1","url_pdf":"http://arxiv.org/pdf/1605.01194v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iiscnlp-at-semeval-2016-task-2-interpretable-1","repo_url":"https://github.com/lavanyats/iMATCH","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sts","task_name":"STS"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-similarity","task_name":"Sentence Similarity"},{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.01194","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}