{"id":"https://openalex.org/W2131876387","doi":"https://doi.org/10.1145/2661829.2661935","title":"A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval","display_name":"A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval","publication_year":2014,"publication_date":"2014-11-03","ids":{"openalex":"https://openalex.org/W2131876387","doi":"https://doi.org/10.1145/2661829.2661935","mag":"2131876387"},"language":"en","primary_location":{"id":"doi:10.1145/2661829.2661935","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661829.2661935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management","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/A5101180037","display_name":"Yelong Shen","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yelong Shen","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101727205","display_name":"Xiaodong He","orcid":"https://orcid.org/0000-0002-9463-9168"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodong He","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114910293","display_name":"Jianfeng Gao","orcid":"https://orcid.org/0000-0002-5702-6143"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianfeng Gao","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100671324","display_name":"Li Deng","orcid":"https://orcid.org/0000-0002-1014-0790"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Deng","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036057896","display_name":"Gr\u00e9goire Mesnil","orcid":null},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gr\u00e9goire Mesnil","raw_affiliation_strings":["University of Montr\u00e9al, Montr\u00e9al, Canada","University of Montreal , Montreal, Canada"],"affiliations":[{"raw_affiliation_string":"University of Montr\u00e9al, Montr\u00e9al, Canada","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"University of Montreal , Montreal, Canada","institution_ids":["https://openalex.org/I70931966"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101180037"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":77.599,"has_fulltext":false,"cited_by_count":685,"citation_normalized_percentile":{"value":0.99949833,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"101","last_page":"110"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9984999895095825,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9962999820709229,"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":0.850670337677002},{"id":"https://openalex.org/keywords/explicit-semantic-analysis","display_name":"Explicit semantic analysis","score":0.7087463140487671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6349539160728455},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5910899639129639},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5368011593818665},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.485809326171875},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.47115790843963623},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4390912353992462},{"id":"https://openalex.org/keywords/probabilistic-latent-semantic-analysis","display_name":"Probabilistic latent semantic analysis","score":0.43315261602401733},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.43278688192367554},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.414247065782547},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.41215670108795166},{"id":"https://openalex.org/keywords/semantic-computing","display_name":"Semantic computing","score":0.34993571043014526},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.28473177552223206},{"id":"https://openalex.org/keywords/semantic-web","display_name":"Semantic Web","score":0.21567082405090332},{"id":"https://openalex.org/keywords/semantic-technology","display_name":"Semantic technology","score":0.11113214492797852}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.850670337677002},{"id":"https://openalex.org/C173862523","wikidata":"https://www.wikidata.org/wiki/Q5421270","display_name":"Explicit semantic analysis","level":5,"score":0.7087463140487671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6349539160728455},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5910899639129639},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5368011593818665},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.485809326171875},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.47115790843963623},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4390912353992462},{"id":"https://openalex.org/C112933361","wikidata":"https://www.wikidata.org/wiki/Q2845258","display_name":"Probabilistic latent semantic analysis","level":2,"score":0.43315261602401733},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.43278688192367554},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.414247065782547},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.41215670108795166},{"id":"https://openalex.org/C511149849","wikidata":"https://www.wikidata.org/wiki/Q7449051","display_name":"Semantic computing","level":3,"score":0.34993571043014526},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.28473177552223206},{"id":"https://openalex.org/C2129575","wikidata":"https://www.wikidata.org/wiki/Q54837","display_name":"Semantic Web","level":2,"score":0.21567082405090332},{"id":"https://openalex.org/C6881194","wikidata":"https://www.wikidata.org/wiki/Q7449091","display_name":"Semantic technology","level":4,"score":0.11113214492797852},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2661829.2661935","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2661829.2661935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1557758455","https://openalex.org/W1576278180","https://openalex.org/W1880262756","https://openalex.org/W1889268436","https://openalex.org/W1985554184","https://openalex.org/W2015441003","https://openalex.org/W2024539680","https://openalex.org/W2036242736","https://openalex.org/W2042980227","https://openalex.org/W2046902062","https://openalex.org/W2062270497","https://openalex.org/W2063397738","https://openalex.org/W2070740689","https://openalex.org/W2072128103","https://openalex.org/W2082718666","https://openalex.org/W2088004917","https://openalex.org/W2097732278","https://openalex.org/W2105008421","https://openalex.org/W2106347453","https://openalex.org/W2107370612","https://openalex.org/W2107743791","https://openalex.org/W2115924763","https://openalex.org/W2128892113","https://openalex.org/W2136189984","https://openalex.org/W2136542423","https://openalex.org/W2136583886","https://openalex.org/W2138806976","https://openalex.org/W2139688392","https://openalex.org/W2141200610","https://openalex.org/W2141599568","https://openalex.org/W2143331230","https://openalex.org/W2147152072","https://openalex.org/W2153579005","https://openalex.org/W2158899491","https://openalex.org/W2160825952","https://openalex.org/W2169213601","https://openalex.org/W2186845332","https://openalex.org/W2250445771","https://openalex.org/W2251008987","https://openalex.org/W2251143283","https://openalex.org/W2289346390","https://openalex.org/W2913932916","https://openalex.org/W2992809543","https://openalex.org/W2997617958","https://openalex.org/W4231109964","https://openalex.org/W4233135949","https://openalex.org/W4240913316","https://openalex.org/W4245107743","https://openalex.org/W4246858749","https://openalex.org/W4251560691","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2103835134","https://openalex.org/W2907506409","https://openalex.org/W2113661533","https://openalex.org/W1984220027","https://openalex.org/W2359259132","https://openalex.org/W2890241594","https://openalex.org/W57213121","https://openalex.org/W2991851652","https://openalex.org/W3152143533","https://openalex.org/W4246972993"],"abstract_inverted_index":{"In":[0,30],"this":[1],"paper,":[2],"we":[3,44],"propose":[4],"a":[5,12,39,42,50,55,89,94,106,131,137],"new":[6],"latent":[7,118],"semantic":[8,21,102,119,151,165],"model":[9,82,120,147],"that":[10,144],"incorporates":[11],"convolutional-pooling":[13],"structure":[14],"over":[15],"word":[16,48,56,65,71,76],"sequences":[17],"to":[18,32,58,87,99,104],"learn":[19],"low-dimensional,":[20],"vector":[22,108],"representations":[23],"for":[24,110,157],"search":[25],"queries":[26,154],"and":[27,83,127,155],"Web":[28,132],"documents.":[29],"order":[31],"capture":[33,60],"the":[34,64,69,75,81,111,145,158],"rich":[35],"contextual":[36,61],"structures":[37],"in":[38,54,74,153],"query":[40],"or":[41],"document,":[43],"start":[45],"with":[46],"each":[47],"within":[49],"temporal":[51],"context":[52],"window":[53],"sequence":[57,77],"directly":[59],"features":[62,73],"at":[63],"n-gram":[66,72],"level.":[67],"Next,":[68],"salient":[70,150],"are":[78,84],"discovered":[79],"by":[80],"then":[85],"aggregated":[86],"form":[88],"sentence-level":[90],"feature":[91],"vector.":[92],"Finally,":[93],"non-linear":[95],"transformation":[96],"is":[97,122,128],"applied":[98],"extract":[100],"high-level":[101],"information":[103,152],"generate":[105],"continuous":[107],"representation":[109],"full":[112],"text":[113],"string.":[114],"The":[115],"proposed":[116,146],"convolutional":[117],"(CLSM)":[121],"trained":[123],"on":[124,130],"clickthrough":[125],"data":[126,140],"evaluated":[129],"document":[133],"ranking":[134],"task":[135,159],"using":[136],"large-scale,":[138],"real-world":[139],"set.":[141],"Results":[142],"show":[143],"effectively":[148],"captures":[149],"documents":[156],"while":[160],"significantly":[161],"outperforming":[162],"previous":[163],"state-of-the-art":[164],"models.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":47},{"year":2022,"cited_by_count":61},{"year":2021,"cited_by_count":90},{"year":2020,"cited_by_count":90},{"year":2019,"cited_by_count":91},{"year":2018,"cited_by_count":80},{"year":2017,"cited_by_count":82},{"year":2016,"cited_by_count":59},{"year":2015,"cited_by_count":32},{"year":2014,"cited_by_count":10}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}