{"id":"https://openalex.org/W2165496929","doi":"https://doi.org/10.3115/v1/d14-1163","title":"Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures","display_name":"Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2165496929","doi":"https://doi.org/10.3115/v1/d14-1163","mag":"2165496929"},"language":"en","primary_location":{"id":"doi:10.3115/v1/d14-1163","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1163","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3115/v1/d14-1163","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054582135","display_name":"Kazuma Hashimoto","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazuma Hashimoto","raw_affiliation_strings":["Univ.\\ of Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ.\\ of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049427071","display_name":"Pontus Stenetorp","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Pontus Stenetorp","raw_affiliation_strings":["Univ.\\ of Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ.\\ of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101412540","display_name":"Makoto Miwa","orcid":"https://orcid.org/0000-0002-2330-6972"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Makoto Miwa","raw_affiliation_strings":["Univ.\\ of Tokyo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ.\\ of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064113904","display_name":"Yoshimasa Tsuruoka","orcid":"https://orcid.org/0000-0002-0707-1077"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshimasa Tsuruoka","raw_affiliation_strings":["Toyota Technological Institute,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute,","institution_ids":["https://openalex.org/I4840577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.6167,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.97871493,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1544","last_page":"1555"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/predicate","display_name":"Predicate (mathematical logic)","score":0.7783814668655396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7614716291427612},{"id":"https://openalex.org/keywords/argument","display_name":"Argument (complex analysis)","score":0.6185860633850098},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5618407726287842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.500922441482544},{"id":"https://openalex.org/keywords/composition","display_name":"Composition (language)","score":0.47246479988098145},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.41163599491119385},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.34190282225608826},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.25749385356903076},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.07961812615394592}],"concepts":[{"id":"https://openalex.org/C140146324","wikidata":"https://www.wikidata.org/wiki/Q1144319","display_name":"Predicate (mathematical logic)","level":2,"score":0.7783814668655396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7614716291427612},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.6185860633850098},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5618407726287842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.500922441482544},{"id":"https://openalex.org/C40231798","wikidata":"https://www.wikidata.org/wiki/Q1333743","display_name":"Composition (language)","level":2,"score":0.47246479988098145},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.41163599491119385},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.34190282225608826},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.25749385356903076},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.07961812615394592},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3115/v1/d14-1163","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1163","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.668.8181","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.668.8181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D14/D14-1163.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.672.4570","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.4570","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://emnlp2014.org/papers/pdf/EMNLP2014163.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.702.1179","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.702.1179","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.logos.t.u-tokyo.ac.jp/%7Ehassy/publications/emnlp2014/paper.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.721.7195","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.7195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://pontus.stenetorp.se/res/pdf/hashimoto2014jointly.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/v1/d14-1163","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1163","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7300000190734863,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W16329575","https://openalex.org/W97201605","https://openalex.org/W618279829","https://openalex.org/W1484288670","https://openalex.org/W1608322251","https://openalex.org/W1614298861","https://openalex.org/W1662133657","https://openalex.org/W1889268436","https://openalex.org/W1965893653","https://openalex.org/W1973942085","https://openalex.org/W1984052055","https://openalex.org/W2038423395","https://openalex.org/W2067438047","https://openalex.org/W2097732278","https://openalex.org/W2110616675","https://openalex.org/W2120861206","https://openalex.org/W2125573226","https://openalex.org/W2133280805","https://openalex.org/W2137607259","https://openalex.org/W2146502635","https://openalex.org/W2149557440","https://openalex.org/W2153579005","https://openalex.org/W2155870214","https://openalex.org/W2158139315","https://openalex.org/W2158899491","https://openalex.org/W2164019165","https://openalex.org/W2171879392","https://openalex.org/W2250275510","https://openalex.org/W2250417532","https://openalex.org/W2251123913","https://openalex.org/W2251771443","https://openalex.org/W2251939518","https://openalex.org/W2252094050","https://openalex.org/W2979401726","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2360025963","https://openalex.org/W2370299677","https://openalex.org/W2611614995","https://openalex.org/W2360785147","https://openalex.org/W2368651715","https://openalex.org/W2789919619","https://openalex.org/W3107474891","https://openalex.org/W1508636238","https://openalex.org/W2171013172","https://openalex.org/W1489720340"],"abstract_inverted_index":{"We":[0,68],"introduce":[1],"a":[2,103],"novel":[3],"compositional":[4],"lan-guage":[5],"model":[6,14,34,50,115],"that":[7],"works":[8],"on":[9],"Predicate-Argument":[10],"Structures":[11],"(PASs).":[12],"Our":[13,89],"jointly":[15],"learns":[16],"word":[17,99],"representations":[18],"and":[19,25,57,65,77,101],"their":[20],"composition":[21],"functions":[22],"using":[23,40,72,102],"bag-of-words":[24],"dependency-based":[26],"con-texts.":[27],"Unlike":[28],"previous":[29],"word-sequence-based":[30],"models,":[31],"our":[32,49,114],"PAS-based":[33],"com-poses":[35],"arguments":[36],"into":[37],"predicates":[38],"by":[39,123],"the":[41,45,85,95,111,118,121],"category":[42],"information":[43],"from":[44],"PAS.":[46],"This":[47],"enables":[48],"to":[51,58,81],"capture":[52],"long-range":[53],"dependencies":[54],"between":[55],"words":[56],"better":[59],"handle":[60],"constructs":[61],"such":[62],"as":[63,124,126],"verb-object":[64],"subject-verb-object":[66,112],"relations.":[67],"verify":[69],"this":[70],"experimentally":[71],"two":[73],"phrase":[74],"similarity":[75],"datasets":[76],"achieve":[78],"re-sults":[79],"comparable":[80],"or":[82],"higher":[83],"than":[84],"pre-vious":[86],"best":[87],"results.":[88],"system":[90],"achieves":[91],"these":[92],"results":[93],"without":[94],"need":[96],"for":[97,110],"pre-trained":[98],"vectors":[100],"much":[104,125],"smaller":[105],"training":[106],"corpus;":[107],"despite":[108],"this,":[109],"dataset":[113],"improves":[116],"upon":[117],"state":[119],"of":[120],"art":[122],"10":[127],"%":[128],"in":[129],"relative":[130],"performance.":[131],"1":[132]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":11}],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}