{"id":"https://openalex.org/W2465801315","doi":"https://doi.org/10.1145/2911451.2914691","title":"Exploiting Semantic Coherence Features for Information Retrieval","display_name":"Exploiting Semantic Coherence Features for Information Retrieval","publication_year":2016,"publication_date":"2016-07-07","ids":{"openalex":"https://openalex.org/W2465801315","doi":"https://doi.org/10.1145/2911451.2914691","mag":"2465801315"},"language":"en","primary_location":{"id":"doi:10.1145/2911451.2914691","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2914691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","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/A5103248641","display_name":"Xinhui Tu","orcid":"https://orcid.org/0009-0000-6570-009X"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinhui Tu","raw_affiliation_strings":["Central China Normal University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000409439","display_name":"Jimmy Xiangji Huang","orcid":"https://orcid.org/0000-0003-1292-1491"},"institutions":[{"id":"https://openalex.org/I192455969","display_name":"York University","ror":"https://ror.org/05fq50484","country_code":"CA","type":"education","lineage":["https://openalex.org/I192455969"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jimmy Xiangji Huang","raw_affiliation_strings":["York University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"York University, Toronto, Canada","institution_ids":["https://openalex.org/I192455969"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100727124","display_name":"Jing Luo","orcid":"https://orcid.org/0000-0003-0432-6908"},"institutions":[{"id":"https://openalex.org/I43922553","display_name":"Wuhan University of Science and Technology","ror":"https://ror.org/00e4hrk88","country_code":"CN","type":"education","lineage":["https://openalex.org/I43922553"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Luo","raw_affiliation_strings":["Wuhan University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I43922553"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039602194","display_name":"Tingting He","orcid":"https://orcid.org/0000-0002-5677-2718"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting He","raw_affiliation_strings":["Central China Normal University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103248641"],"corresponding_institution_ids":["https://openalex.org/I40963666"],"apc_list":null,"apc_paid":null,"fwci":1.7139,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88825541,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"837","last_page":"840"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9986000061035156,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9986000061035156,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9973999857902527,"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/computer-science","display_name":"Computer science","score":0.84126216173172},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.755842924118042},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.6729406714439392},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.6434434652328491},{"id":"https://openalex.org/keywords/term-discrimination","display_name":"Term Discrimination","score":0.6000105142593384},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.5612176060676575},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.5392706394195557},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.45192596316337585},{"id":"https://openalex.org/keywords/explicit-semantic-analysis","display_name":"Explicit semantic analysis","score":0.44435450434684753},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4037523567676544},{"id":"https://openalex.org/keywords/concept-search","display_name":"Concept search","score":0.21563482284545898},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.2044382095336914},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.1626833975315094},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.1397797167301178},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.09542062878608704},{"id":"https://openalex.org/keywords/semantic-technology","display_name":"Semantic technology","score":0.08243978023529053},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07745710015296936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06334325671195984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.84126216173172},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.755842924118042},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6729406714439392},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.6434434652328491},{"id":"https://openalex.org/C22639730","wikidata":"https://www.wikidata.org/wiki/Q7702546","display_name":"Term Discrimination","level":5,"score":0.6000105142593384},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.5612176060676575},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.5392706394195557},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.45192596316337585},{"id":"https://openalex.org/C173862523","wikidata":"https://www.wikidata.org/wiki/Q5421270","display_name":"Explicit semantic analysis","level":5,"score":0.44435450434684753},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4037523567676544},{"id":"https://openalex.org/C182861755","wikidata":"https://www.wikidata.org/wiki/Q5158391","display_name":"Concept search","level":4,"score":0.21563482284545898},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.2044382095336914},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.1626833975315094},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.1397797167301178},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.09542062878608704},{"id":"https://openalex.org/C6881194","wikidata":"https://www.wikidata.org/wiki/Q7449091","display_name":"Semantic technology","level":4,"score":0.08243978023529053},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07745710015296936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06334325671195984},{"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},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2911451.2914691","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2911451.2914691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1525595230","https://openalex.org/W1854214752","https://openalex.org/W1956559956","https://openalex.org/W1972594981","https://openalex.org/W1979963107","https://openalex.org/W1981038351","https://openalex.org/W1990589796","https://openalex.org/W2009593660","https://openalex.org/W2010323118","https://openalex.org/W2052842594","https://openalex.org/W2093390569","https://openalex.org/W2099871636","https://openalex.org/W2129319055","https://openalex.org/W2134557008","https://openalex.org/W2150071570","https://openalex.org/W2153252192","https://openalex.org/W2155482025","https://openalex.org/W2160885631","https://openalex.org/W2165612380","https://openalex.org/W4206765718","https://openalex.org/W4252076394"],"related_works":["https://openalex.org/W2145036943","https://openalex.org/W2049534074","https://openalex.org/W2000031603","https://openalex.org/W2105363053","https://openalex.org/W4200247715","https://openalex.org/W2102270039","https://openalex.org/W181681892","https://openalex.org/W1974970223","https://openalex.org/W3028990185","https://openalex.org/W309385283"],"abstract_inverted_index":{"Most":[0],"of":[1,11,17,31,33,48,57,79,100,103],"the":[2,9,29,46,49,52,55,68,77,83,86,98,104,114,125,130],"existing":[3],"information":[4,115],"retrieval":[5,21,116],"models":[6,22,127],"assume":[7],"that":[8],"terms":[10,106],"a":[12,34,37,58,61,93,108],"text":[13],"document":[14,40,43,62,109],"are":[15],"independent":[16],"each":[18],"other.":[19],"These":[20],"integrate":[23],"three":[24,69],"major":[25],"variables":[26],"to":[27,112],"determine":[28],"degree":[30,78,99],"importance":[32,56],"term":[35,41,50,59,84],"for":[36,60],"document:":[38],"within":[39],"frequency,":[42],"length":[44],"and":[45,85],"specificity":[47],"in":[51,96],"collection.":[53],"Intuitively,":[54],"is":[63,110],"not":[64],"only":[65],"dependent":[66,75],"on":[67,76,120],"aspects":[70],"mentioned":[71],"above,":[72],"but":[73],"also":[74],"semantic":[80,101],"coherence":[81,102],"between":[82],"document.":[87],"In":[88],"this":[89],"paper,":[90],"we":[91],"propose":[92],"heuristic":[94],"approach,":[95],"which":[97],"query":[105],"with":[107],"adopted":[111],"improve":[113],"performance.":[117],"Experimental":[118],"results":[119],"standard":[121],"TREC":[122],"collections":[123],"show":[124],"proposed":[126],"consistently":[128],"outperform":[129],"state-of-the-art":[131],"models.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}