{"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/distinguishing-antonyms-and-synonyms-in-a","title":"Distinguishing Antonyms and Synonyms in a Pattern-based Neural Network","arxiv_id":"1701.02962","date":"2017-01-11","proceeding":"EACL 2017 4","authors":["Kim Anh Nguyen","Sabine Schulte im Walde","Ngoc Thang Vu"],"abstract":"Distinguishing between antonyms and synonyms is a key task to achieve high\nperformance in NLP systems. While they are notoriously difficult to distinguish\nby distributional co-occurrence models, pattern-based methods have proven\neffective to differentiate between the relations. In this paper, we present a\nnovel neural network model AntSynNET that exploits lexico-syntactic patterns\nfrom syntactic parse trees. In addition to the lexical and syntactic\ninformation, we successfully integrate the distance between the related words\nalong the syntactic path as a new pattern feature. The results from\nclassification experiments show that AntSynNET improves the performance over\nprior pattern-based methods.","url_abs":"http://arxiv.org/abs/1701.02962v1","url_pdf":"http://arxiv.org/pdf/1701.02962v1.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":"distinguishing-antonyms-and-synonyms-in-a","repo_url":"https://github.com/nguyenkh/AntSynNET","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1701.02962","atlas_url":"https://app.syntology.ai/?focus=1701.02962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.02962"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nguyenkh/AntSynNET","reach":null}],"summary":{"ran_draft_wrong":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":1,"samples":[{"code_sha256_prefix":"35448309e43583b0","entry":"unzip","repo":"nguyenkh/AntSynNET","repo_kind":"official","path":"train_ant_syn_net.py","file_url":"https://github.com/nguyenkh/AntSynNET/blob/HEAD/train_ant_syn_net.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"35448309e43583b0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}