{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:36:03Z","timestamp":1784914563729,"version":"3.55.0"},"reference-count":87,"publisher":"MIT Press","issue":"1","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Current open-domain neural semantics parsers show impressive performance. However, closer inspection of the symbolic meaning representations they produce reveals significant weaknesses: Sometimes they tend to merely copy character sequences from the source text to form symbolic concepts, defaulting to the most frequent word sense based in the training distribution. By leveraging the hierarchical structure of a lexical ontology, we introduce a novel compositional symbolic representation for concepts based on their position in the taxonomical hierarchy. This representation provides richer semantic information and enhances interpretability. We introduce a neural \u201ctaxonomical\u201d semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic parser trained on the traditional meaning representation format, employing a novel challenge set and evaluation metric for evaluation. Our experimental findings demonstrate that the taxonomical model, trained on much richer and complex meaning representations, is slightly subordinate in performance to the traditional model using the standard metrics for evaluation, but outperforms it when dealing with out-of-vocabulary concepts. We further show through neural model probing that training on a taxonomic representation enhances the model\u2019s ability to learn the taxonomical hierarchy. This finding is encouraging for research in computational semantics that aims to combine data-driven distributional meanings with knowledge-based symbolic representations.<\/jats:p>","DOI":"10.1162\/coli_a_00542","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T20:36:07Z","timestamp":1728074167000},"page":"235-274","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":8,"title":["Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations"],"prefix":"10.1162","volume":"51","author":[{"given":"Xiao","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Groningen, Center for Language and Cognition. xiao.zhang@rug.nl"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gosse","family":"Bouma","sequence":"additional","affiliation":[{"name":"University of Groningen, Center for Language and Cognition. g.bouma@rug.nl"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johan","family":"Bos","sequence":"additional","affiliation":[{"name":"University of Groningen, Center for Language and Cognition. johan.bos@rug.nl"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2025,3,15]]},"reference":[{"key":"2025032113505082700_bib1","doi-asserted-by":"publisher","first-page":"242","DOI":"10.18653\/v1\/E17-2039","article-title":"The Parallel Meaning Bank: Towards a multilingual corpus of translations annotated with compositional meaning representations","volume-title":"Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers","author":"Abzianidze","year":"2017"},{"key":"2025032113505082700_bib2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.18653\/v1\/2020.conll-shared.2","article-title":"DRS at MRP 2020: Dressing up discourse representation structures as graphs","volume-title":"Proceedings of the CoNNL 2020 Shared Task: Cross-Framework Meaning Representation Parsing","author":"Abzianidze","year":"2020"},{"key":"2025032113505082700_bib3","doi-asserted-by":"publisher","first-page":"343","DOI":"10.3115\/1626481.1626508","article-title":"Deep semantic analysis of text","volume-title":"Semantics in Text Processing. 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