{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:57:34Z","timestamp":1785416254907,"version":"3.56.0"},"publisher-location":"Amsterdam","reference-count":35,"publisher":"John Benjamins Publishing Company","isbn-type":[{"value":"9789027209900","type":"print"},{"value":"9789027258489","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    We investigate the feasibility of automatic\n                    <jats:italic>semantic role labeling<\/jats:italic>\n                    (SRL) using Swedish\n\t\t\t\t\t\t\t\tFrameNet (SweFN). In the first part of the chapter, we describe a\n\t\t\t\t\t\t\t\tbaseline system using a traditional division into segmentation and\n\t\t\t\t\t\t\t\tlabeling steps. These subsystems are implemented as separate machine\n\t\t\t\t\t\t\t\tlearning models, and we explore a wide range of syntactic and\n\t\t\t\t\t\t\t\tlexical features for these models. In the second part, we turn to\n\t\t\t\t\t\t\t\tthe question of how the frame-to-frame relations defined in FrameNet\n\t\t\t\t\t\t\t\tallow us to use the annotated examples more effectively. The\n\t\t\t\t\t\t\t\tcross-frame generalization methods reduce the number of errors made\n\t\t\t\t\t\t\t\tby the labeling classifier by 27%. For previously unseen frames, the\n\t\t\t\t\t\t\t\treduction is even more significant: 50%.\n                  <\/jats:p>","DOI":"10.1075\/nlp.14.10joh","type":"book-chapter","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T09:12:16Z","timestamp":1636449136000},"page":"263-280","source":"Crossref","is-referenced-by-count":1,"title":["Semantic role labeling"],"prefix":"10.1075","author":[{"given":"Richard","family":"Johansson","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01tm6cn81","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Gothenburg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karin","family":"Friberg Heppin","sequence":"additional","affiliation":[{"name":"HeppiLing AB"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Kokkinakis","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01tm6cn81","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Gothenburg"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1757","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"CIT0563","first-page":"86","article-title":"The Berkeley FrameNet project","volume-title":"Proceedings of Coling 1998","author":"Baker","year":"1998"},{"key":"CIT0564","first-page":"269","article-title":"The past meets the present in Swedish FrameNet++","volume-title":"Proceedings of EURALEX 2010","author":"Borin","year":"2010"},{"key":"CIT0565","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-013-9233-4"},{"issue":"4","key":"CIT0566","first-page":"467","article-title":"Class-based n-gram models of natural language","volume":"18","author":"Brown","year":"1992","journal-title":"Computational Linguistics"},{"key":"CIT0567","first-page":"560","article-title":"Fast semantic extraction using a novel neural network architecture","volume-title":"Proceedings of ACL 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