{"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/cat-a-contextualized-conceptualization-and","title":"CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning","arxiv_id":"2305.04808","date":"2023-05-08","proceeding":null,"authors":["Weiqi Wang","Tianqing Fang","Baixuan Xu","Chun Yi Louis Bo","Yangqiu Song","Lei Chen"],"abstract":"Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize. For someone who barely knows about \"meditation,\" while is knowledgeable about \"singing,\" he can still infer that \"meditation makes people relaxed\" from the existing knowledge that \"singing makes people relaxed\" by first conceptualizing \"singing\" as a \"relaxing event\" and then instantiating that event to \"meditation.\" This process, known as conceptual induction and deduction, is fundamental to commonsense reasoning while lacking both labeled data and methodologies to enhance commonsense modeling. To fill such a research gap, we propose CAT (Contextualized ConceptuAlization and InsTantiation), a semi-supervised learning framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale. Extensive experiments show that our framework achieves state-of-the-art performances on two conceptualization tasks, and the acquired abstract commonsense knowledge can significantly improve commonsense inference modeling. Our code, data, and fine-tuned models are publicly available at https://github.com/HKUST-KnowComp/CAT.","url_abs":"https://arxiv.org/abs/2305.04808v2","url_pdf":"https://arxiv.org/pdf/2305.04808v2.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":"cat-a-contextualized-conceptualization-and","repo_url":"https://github.com/hkust-knowcomp/cat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"cat-a-contextualized-conceptualization-and","repo_url":"https://github.com/hkust-knowcomp/car","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.04808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04808"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/HKUST-KnowComp/CAT","reach":null}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"3b569acd021644d5","entry":"ConceptLinkingClassifier","repo":"HKUST-KnowComp/CAT","repo_kind":"official","path":"source/event_concept_discrimination/model.py","file_url":"https://github.com/HKUST-KnowComp/CAT/blob/HEAD/source/event_concept_discrimination/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3b569acd021644d5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}