{"id":"https://openalex.org/W2106224734","doi":"https://doi.org/10.3115/v1/p14-2101","title":"Automatic Labelling of Topic Models Learned from Twitter by Summarisation","display_name":"Automatic Labelling of Topic Models Learned from Twitter by Summarisation","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2106224734","doi":"https://doi.org/10.3115/v1/p14-2101","mag":"2106224734"},"language":"en","primary_location":{"id":"doi:10.3115/v1/p14-2101","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-2101","pdf_url":"https://aclanthology.org/P14-2101.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 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/P14-2101.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008349559","display_name":"Amparo Elizabeth Cano Basave","orcid":null},"institutions":[{"id":"https://openalex.org/I8679417","display_name":"Hong Kong Metropolitan University","ror":"https://ror.org/0349bsm71","country_code":"HK","type":"education","lineage":["https://openalex.org/I8679417"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Amparo Elizabeth Cano Basave","raw_affiliation_strings":["Open University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Open University","institution_ids":["https://openalex.org/I8679417"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015709853","display_name":"Yulan He","orcid":"https://orcid.org/0000-0003-3948-5845"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yulan He","raw_affiliation_strings":["Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026719663","display_name":"Ruifeng Xu","orcid":"https://orcid.org/0000-0002-4009-5679"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruifeng Xu","raw_affiliation_strings":["Harbin Institute of Technology (Shenzhen)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology (Shenzhen)","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":53,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"618","last_page":"624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"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":0.9997000098228455,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9980999827384949,"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":0.994700014591217,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.9401708245277405},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.851115345954895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.817471444606781},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6188474893569946},{"id":"https://openalex.org/keywords/labelling","display_name":"Labelling","score":0.5780057907104492},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5680709481239319},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.45905956625938416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4279107451438904},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4269232451915741},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3348464369773865},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.32525113224983215},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.22722360491752625}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.9401708245277405},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.851115345954895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.817471444606781},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6188474893569946},{"id":"https://openalex.org/C2780523633","wikidata":"https://www.wikidata.org/wiki/Q380709","display_name":"Labelling","level":2,"score":0.5780057907104492},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5680709481239319},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.45905956625938416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4279107451438904},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4269232451915741},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3348464369773865},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32525113224983215},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.22722360491752625},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C73484699","wikidata":"https://www.wikidata.org/wiki/Q161733","display_name":"Criminology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3115/v1/p14-2101","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-2101","pdf_url":"https://aclanthology.org/P14-2101.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 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:oro.open.ac.uk:41413","is_oa":false,"landing_page_url":"https://oro.open.ac.uk/41413/1/P14-2101","pdf_url":null,"source":{"id":"https://openalex.org/S4377196284","display_name":"Open Research Online - ORO (The Open University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204136569","host_organization_name":"The Open University","host_organization_lineage":["https://openalex.org/I204136569"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"}],"best_oa_location":{"id":"doi:10.3115/v1/p14-2101","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/p14-2101","pdf_url":"https://aclanthology.org/P14-2101.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 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2106224734.pdf"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W100656083","https://openalex.org/W152260871","https://openalex.org/W1501617060","https://openalex.org/W1525595230","https://openalex.org/W1566963451","https://openalex.org/W1880262756","https://openalex.org/W1882070667","https://openalex.org/W1975579663","https://openalex.org/W2001082470","https://openalex.org/W2062432826","https://openalex.org/W2066636486","https://openalex.org/W2069667724","https://openalex.org/W2082512208","https://openalex.org/W2083305840","https://openalex.org/W2087045154","https://openalex.org/W2097807612","https://openalex.org/W2098162425","https://openalex.org/W2102230199","https://openalex.org/W2110921901","https://openalex.org/W2113499583","https://openalex.org/W2113855231","https://openalex.org/W2120113177","https://openalex.org/W2120481102","https://openalex.org/W2123495014","https://openalex.org/W2130339025","https://openalex.org/W2137553870","https://openalex.org/W2141927646","https://openalex.org/W2147946282","https://openalex.org/W2154652894","https://openalex.org/W2167232041","https://openalex.org/W2250925602","https://openalex.org/W2251582277","https://openalex.org/W2535726973"],"related_works":["https://openalex.org/W2888805565","https://openalex.org/W4312773271","https://openalex.org/W4315588616","https://openalex.org/W2769501189","https://openalex.org/W2962686197","https://openalex.org/W2207653751","https://openalex.org/W3159709618","https://openalex.org/W2611137333","https://openalex.org/W3005513013","https://openalex.org/W2122605835"],"abstract_inverted_index":{"Latent":[0,8],"topics":[1,31,41,72,93],"derived":[2,32],"by":[3,168],"topic":[4,53,111,156],"models":[5],"such":[6,30],"as":[7,97],"Dirichlet":[9],"Allocation":[10],"(LDA)":[11],"are":[12,115],"the":[13,24,48,70,86,124,133,138,144,164],"result":[14],"of":[15,29,69,88,91,117,126,140,143],"hidden":[16],"thematic":[17],"structures":[18],"which":[19,56,105,158],"provide":[20],"further":[21],"insights":[22],"into":[23],"data.":[25],"The":[26],"automatic":[27,52,89],"labelling":[28,54,90],"from":[33,95],"social":[34],"media":[35],"poses":[36],"however":[37],"new":[38],"challenges":[39],"since":[40,66],"may":[42,73],"characterise":[43],"novel":[44],"events":[45],"happening":[46],"in":[47,76,129],"real":[49],"world.":[50],"Existing":[51],"approaches":[55],"depend":[57],"on":[58,123],"external":[59,77,118],"knowledge":[60],"sources":[61,119],"become":[62],"less":[63],"applicable":[64],"here":[65],"relevant":[67],"articles/concepts":[68],"extracted":[71],"not":[74],"exist":[75],"sources.":[78],"In":[79],"this":[80],"paper":[81],"we":[82],"propose":[83],"to":[84,109,132,163],"address":[85],"problem":[87],"latent":[92,134],"learned":[94],"Twitter":[96],"a":[98,103],"summarisation":[99,107,146,152],"problem.":[100],"We":[101,136],"introduce":[102],"framework":[104],"apply":[106],"algorithms":[108,114,153],"generate":[110,154],"labels.":[112],"These":[113],"independent":[116],"and":[120],"only":[121],"rely":[122],"identification":[125],"dominant":[127],"terms":[128,166],"documents":[130],"related":[131],"topic.":[135],"compare":[137],"efficiency":[139],"existing":[141],"state":[142],"art":[145],"algorithms.":[147],"Our":[148],"results":[149],"suggest":[150],"that":[151],"better":[155],"labels":[157],"capture":[159],"event-related":[160],"context":[161],"compared":[162],"top-n":[165],"returned":[167],"LDA.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
