{"id":"https://openalex.org/W2906834561","doi":"https://doi.org/10.1109/icdm.2018.00073","title":"ASTM: An Attentional Segmentation Based Topic Model for Short Texts","display_name":"ASTM: An Attentional Segmentation Based Topic Model for Short Texts","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2906834561","doi":"https://doi.org/10.1109/icdm.2018.00073","mag":"2906834561"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2018.00073","doi":"https://doi.org/10.1109/icdm.2018.00073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2018.00073","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072042122","display_name":"Jiamiao Wang","orcid":"https://orcid.org/0000-0002-1810-5199","observed_orcids":["https://orcid.org/0000-0002-1810-5199"]},"institutions":[{"id":"https://openalex.org/I16365422","display_name":"Hefei University of Technology","ror":"https://ror.org/02czkny70","country_code":"CN","type":"education","lineage":["https://openalex.org/I16365422"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiamiao Wang","raw_affiliation_strings":["School of Computer Science and Information Engineering, Hefei University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Information Engineering, Hefei University of Technology, China","institution_ids":["https://openalex.org/I16365422"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069988750","display_name":"Ling Chen","orcid":"https://orcid.org/0000-0002-6468-5729","observed_orcids":["https://orcid.org/0000-0002-6468-5729","https://orcid.org/0000-0002-1758-0465","https://orcid.org/0000-0002-1725-4942"]},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ling Chen","raw_affiliation_strings":["Centre for Artificial Intelligence, University of Technology, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046220908","display_name":"Lu Qin","orcid":"https://orcid.org/0000-0001-6068-5062","observed_orcids":["https://orcid.org/0000-0001-6068-5062"]},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lu Qin","raw_affiliation_strings":["Centre for Artificial Intelligence, University of Technology, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080738591","display_name":"Xindong Wu","orcid":"https://orcid.org/0000-0003-2396-1704","observed_orcids":["https://orcid.org/0000-0003-2396-1704"]},"institutions":[{"id":"https://openalex.org/I4401726980","display_name":"Mininglamp (China)","ror":"https://ror.org/04tb90x61","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726980"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xindong Wu","raw_affiliation_strings":["MiningLamp Software Systems, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MiningLamp Software Systems, Beijing, China","institution_ids":["https://openalex.org/I4401726980"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6192,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.7258774,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"577","last_page":"586"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9951000213623047,"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":0.9951000213623047,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.0005000000237487257,"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/topic-modeling","display_name":"topic modeling","score":0.8009999990463257},{"id":"https://openalex.org/keywords/short-text","display_name":"short text","score":0.875},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.921999990940094},{"id":"https://openalex.org/keywords/word-embeddings","display_name":"word embeddings","score":0.902999997138977},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.7229999899864197},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"text segmentation","score":0.7009999752044678},{"id":"https://openalex.org/keywords/topic-coherence","display_name":"topic coherence","score":0.8740000128746033},{"id":"https://openalex.org/keywords/text-classification","display_name":"text classification","score":0.8389999866485596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8319603204727173},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.655876636505127},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.648583710193634},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6286835074424744},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6257535219192505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6222968101501465},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.605265736579895},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.5430372357368469},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5329630970954895},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5069966316223145},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3510977029800415},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.15293365716934204},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdm.2018.00073","doi":"https://doi.org/10.1109/icdm.2018.00073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2018.00073","pdf_url":null,"source":{"id":"https://openalex.org/S4210214883","display_name":"Proceedings","issn_l":"1550-4786","issn":["1550-4786","2374-8486"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Data Mining (ICDM)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/133357","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/133357","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W65194033","https://openalex.org/W1498436455","https://openalex.org/W1843891098","https://openalex.org/W1880262756","https://openalex.org/W1902237438","https://openalex.org/W2038889057","https://openalex.org/W2048195127","https://openalex.org/W2051434435","https://openalex.org/W2061922307","https://openalex.org/W2097089247","https://openalex.org/W2118463056","https://openalex.org/W2133564696","https://openalex.org/W2153579005","https://openalex.org/W2171836785","https://openalex.org/W2178725228","https://openalex.org/W2222893162","https://openalex.org/W2250539671","https://openalex.org/W2340381866","https://openalex.org/W2585828887","https://openalex.org/W2604706042","https://openalex.org/W2626778328","https://openalex.org/W2702896255","https://openalex.org/W2731449405","https://openalex.org/W2735674392","https://openalex.org/W2741172726","https://openalex.org/W2744007523","https://openalex.org/W2963436889","https://openalex.org/W2963726741","https://openalex.org/W2964308564"],"related_works":["https://openalex.org/W2946739205","https://openalex.org/W3005792683","https://openalex.org/W2905234665","https://openalex.org/W3154475157","https://openalex.org/W2755330151","https://openalex.org/W3163510162","https://openalex.org/W2340381866","https://openalex.org/W4404792876","https://openalex.org/W2963726741","https://openalex.org/W4379879802"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,116,135],"data":[3],"sparsity":[4],"problem":[5],"in":[6,33,54,137],"short":[7,89,124],"text":[8,90,125,144],"understanding,":[9],"various":[10],"alternative":[11],"topic":[12,31,106,141],"models":[13,26],"leveraging":[14],"word":[15,78],"embeddings":[16,79],"as":[17,80],"background":[18],"knowledge":[19],"have":[20,46,111],"been":[21],"developed":[22],"recently.":[23],"However,":[24],"existing":[25],"combine":[27],"auxiliary":[28],"information":[29,82],"and":[30,83,107,143],"modeling":[32],"a":[34,66,105],"straightforward":[35],"way":[36],"without":[37],"considering":[38],"human":[39,61],"reading":[40],"habits.":[41],"In":[42],"contrast,":[43],"extensive":[44],"studies":[45],"proven":[47],"that":[48,87,131],"it":[49],"is":[50,102],"full":[51],"of":[52,94,118,139],"potential":[53],"textual":[55],"analysis":[56],"by":[57],"taking":[58],"into":[59,92],"account":[60],"attention.":[62,99],"Therefore,":[63],"we":[64],"propose":[65],"novel":[67],"model,":[68],"Attentional":[69],"Segmentation":[70],"based":[71],"Topic":[72],"Model":[73],"(ASTM),":[74],"to":[75,104],"integrate":[76],"both":[77,140],"supplementary":[81],"an":[84],"attention":[85],"mechanism":[86],"segments":[88],"documents":[91],"fragments":[93],"adjacent":[95],"words":[96],"receiving":[97],"similar":[98],"Each":[100],"segment":[101],"assigned":[103],"each":[108],"document":[109],"can":[110],"multiple":[112],"topics.":[113],"We":[114],"evaluate":[115],"performance":[117],"our":[119,132],"model":[120,133],"on":[121],"three":[122],"real-world":[123],"datasets.":[126],"The":[127],"experimental":[128],"results":[129],"demonstrate":[130],"outperforms":[134],"state-of-the-art":[136],"terms":[138],"coherence":[142],"classification.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2019-01-11T00:00:00"}
