{"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/evaluating-scoped-meaning-representations","title":"Evaluating Scoped Meaning Representations","arxiv_id":"1802.08599","date":"2018-02-23","proceeding":"LREC 2018 5","authors":["Rik van Noord","Lasha Abzianidze","Hessel Haagsma","Johan Bos"],"abstract":"Semantic parsing offers many opportunities to improve natural language\nunderstanding. We present a semantically annotated parallel corpus for English,\nGerman, Italian, and Dutch where sentences are aligned with scoped meaning\nrepresentations in order to capture the semantics of negation, modals,\nquantification, and presupposition triggers. The semantic formalism is based on\nDiscourse Representation Theory, but concepts are represented by WordNet\nsynsets and thematic roles by VerbNet relations. Translating scoped meaning\nrepresentations to sets of clauses enables us to compare them for the purpose\nof semantic parser evaluation and checking translations. This is done by\ncomputing precision and recall on matching clauses, in a similar way as is done\nfor Abstract Meaning Representations. We show that our matching tool for\nevaluating scoped meaning representations is both accurate and efficient.\nApplying this matching tool to three baseline semantic parsers yields F-scores\nbetween 43% and 54%. A pilot study is performed to automatically find changes\nin meaning by comparing meaning representations of translations. This\ncomparison turns out to be an additional way of (i) finding annotation mistakes\nand (ii) finding instances where our semantic analysis needs to be improved.","url_abs":"http://arxiv.org/abs/1802.08599v2","url_pdf":"http://arxiv.org/pdf/1802.08599v2.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":"evaluating-scoped-meaning-representations","repo_url":"https://github.com/RikVN/DRS_parsing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"evaluating-scoped-meaning-representations","repo_url":"https://github.com/RikVN/Neural_DRS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"negation","task_name":"Negation"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08599","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}