{"id":"https://openalex.org/W2474198877","doi":"https://doi.org/10.18653/v1/p16-1134","title":"Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields","display_name":"Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2474198877","doi":"https://doi.org/10.18653/v1/p16-1134","mag":"2474198877"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p16-1134","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1134","pdf_url":"https://www.aclweb.org/anthology/P16-1134.pdf","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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P16-1134.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088563455","display_name":"Jingwei Zhuo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingwei Zhuo","raw_affiliation_strings":["Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China","Microsoft Research, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Microsoft Research, Beijing, 100084, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103023862","display_name":"Yong Cao","orcid":"https://orcid.org/0000-0001-7639-7647"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Cao","raw_affiliation_strings":["Microsoft Research, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, 100084, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100606995","display_name":"Jun Zhu","orcid":"https://orcid.org/0000-0002-6254-2388"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Zhu","raw_affiliation_strings":["Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100649544","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-6908-9280"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., TNList Lab, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047496977","display_name":"Zaiqing Nie","orcid":"https://orcid.org/0000-0002-1134-2343"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zaiqing Nie","raw_affiliation_strings":["Microsoft Research, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Beijing, 100084, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5047496977"],"corresponding_institution_ids":["https://openalex.org/I4210113369"],"apc_list":null,"apc_paid":null,"fwci":7.5095,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":{"value":0.97227565,"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":"1413","last_page":"1423"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9973999857902527,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/crfs","display_name":"CRFS","score":0.9912214279174805},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.9144127368927002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8011446595191956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.72406005859375},{"id":"https://openalex.org/keywords/sequence-labeling","display_name":"Sequence labeling","score":0.6728644967079163},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6062425971031189},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4914320707321167},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.48898327350616455},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.44560515880584717},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.4404413104057312},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.43886175751686096},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.43443551659584045},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.41482362151145935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4070969820022583},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2638090252876282},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21704062819480896},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.1456373929977417},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.09658047556877136}],"concepts":[{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.9912214279174805},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.9144127368927002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8011446595191956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.72406005859375},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.6728644967079163},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6062425971031189},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4914320707321167},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.48898327350616455},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.44560515880584717},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.4404413104057312},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.43886175751686096},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.43443551659584045},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.41482362151145935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4070969820022583},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2638090252876282},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21704062819480896},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.1456373929977417},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.09658047556877136},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p16-1134","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1134","pdf_url":"https://www.aclweb.org/anthology/P16-1134.pdf","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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p16-1134","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1134","pdf_url":"https://www.aclweb.org/anthology/P16-1134.pdf","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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1029060068","display_name":null,"funder_award_id":"141080","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2197472058","display_name":null,"funder_award_id":"2013CB329403","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2711980796","display_name":null,"funder_award_id":"61332007","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2802911279","display_name":null,"funder_award_id":"Young","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3211942645","display_name":null,"funder_award_id":"61322308","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3480233651","display_name":null,"funder_award_id":"13223","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G562922687","display_name":null,"funder_award_id":"61332007","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6194230105","display_name":null,"funder_award_id":"2013CB","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6737610855","display_name":"Advanced Research Training in Physiology - Integrating Biology Across the Scales","funder_award_id":"2014108","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7129907147","display_name":null,"funder_award_id":"(973 Program","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7373123958","display_name":null,"funder_award_id":"2013CB329403","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7608752429","display_name":null,"funder_award_id":"Talent","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7733967793","display_name":null,"funder_award_id":"61332","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7905381702","display_name":"I/UCRC FRP:  Collaborative Research: The Physical Internet for a Sustainable Logistics Future - Advancing CELDi's Leadership Position","funder_award_id":"1332007","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G825541959","display_name":null,"funder_award_id":"61332007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320330357","display_name":"Tsinghua Initiative Scientific Research Program","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2474198877.pdf","grobid_xml":"https://content.openalex.org/works/W2474198877.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1623072288","https://openalex.org/W1632114991","https://openalex.org/W1675450783","https://openalex.org/W1681397005","https://openalex.org/W1940872118","https://openalex.org/W2004763266","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2098921539","https://openalex.org/W2102301788","https://openalex.org/W2121227244","https://openalex.org/W2130581319","https://openalex.org/W2144578941","https://openalex.org/W2146502635","https://openalex.org/W2147880316","https://openalex.org/W2150102617","https://openalex.org/W2154326713","https://openalex.org/W2158049734","https://openalex.org/W2158139315","https://openalex.org/W2158188757","https://openalex.org/W2158899491","https://openalex.org/W2167138081","https://openalex.org/W2168596788","https://openalex.org/W2251362855","https://openalex.org/W2951299559","https://openalex.org/W2951562155","https://openalex.org/W2952087486","https://openalex.org/W2952230511","https://openalex.org/W2964199361","https://openalex.org/W2964266863","https://openalex.org/W3158986179","https://openalex.org/W4294027320"],"related_works":["https://openalex.org/W1964783010","https://openalex.org/W2962906565","https://openalex.org/W4250494529","https://openalex.org/W2399696375","https://openalex.org/W2798423868","https://openalex.org/W2061834489","https://openalex.org/W189110383","https://openalex.org/W2062502130","https://openalex.org/W2132038179","https://openalex.org/W2027233318"],"abstract_inverted_index":{"Most":[0],"of":[1,47],"the":[2],"sequence":[3],"tagging":[4],"tasks":[5],"in":[6,21],"natural":[7],"language":[8],"processing":[9],"require":[10],"to":[11],"recognize":[12],"segments":[13,56,79],"with":[14,28],"certain":[15],"syntactic":[16],"role":[17],"or":[18],"semantic":[19],"meaning":[20],"a":[22,66,87],"sentence.":[23],"They":[24],"are":[25],"usually":[26],"tackled":[27],"Conditional":[29,51],"Random":[30,52],"Fields":[31,53],"(CRFs),":[32],"which":[33,77],"do":[34],"indirect":[35],"word-level":[36,39],"modeling":[37],"over":[38],"features":[40,61,85],"and":[41,81,98],"thus":[42],"cannot":[43],"make":[44],"full":[45],"use":[46],"segment-level":[48,60],"information.":[49],"Semi-Markov":[50],"(Semi-CRFs)":[54],"model":[55,78],"directly":[57,80],"but":[58],"extracting":[59],"for":[62],"Semi-CRFs":[63,75],"is":[64],"still":[65],"very":[67],"challenging":[68],"problem.":[69],"This":[70],"paper":[71],"presents":[72],"Gated":[73],"Recursive":[74],"(grSemi-CRFs),":[76],"automatically":[82],"learn":[83],"segmentlevel":[84],"through":[86],"gated":[88],"recursive":[89],"convolutional":[90],"neural":[91,109],"network.":[92],"Our":[93],"experiments":[94],"on":[95],"text":[96],"chunking":[97],"named":[99],"entity":[100],"recognition":[101],"(NER)":[102],"demonstrate":[103],"that":[104],"grSemi-CRFs":[105],"generally":[106],"outperform":[107],"other":[108],"models.":[110]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
