{"id":"https://openalex.org/W2982546493","doi":"https://doi.org/10.1145/3458553.3458569","title":"Neural models for information retrieval without labeled data","display_name":"Neural models for information retrieval without labeled data","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W2982546493","doi":"https://doi.org/10.1145/3458553.3458569","mag":"2982546493"},"language":"en","primary_location":{"id":"doi:10.1145/3458553.3458569","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3458553.3458569","pdf_url":null,"source":{"id":"https://openalex.org/S6756005","display_name":"ACM SIGIR Forum","issn_l":"0163-5840","issn":["0163-5840","1558-0229"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGIR Forum","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://scholarworks.umass.edu/dissertations_2/1787","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101457713","display_name":"Hamed Zamani","orcid":"https://orcid.org/0000-0002-0800-3340"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hamed Zamani","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101457713"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.384,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72856701,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"53","issue":"2","first_page":"104","last_page":"105"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9991999864578247,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9072375297546387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6540488600730896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5858716368675232},{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.5666418075561523},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.555512011051178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.514013409614563},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5002696514129639},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44835054874420166},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44373154640197754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9072375297546387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6540488600730896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5858716368675232},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.5666418075561523},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.555512011051178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.514013409614563},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5002696514129639},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44835054874420166},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44373154640197754},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3458553.3458569","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3458553.3458569","pdf_url":null,"source":{"id":"https://openalex.org/S6756005","display_name":"ACM SIGIR Forum","issn_l":"0163-5840","issn":["0163-5840","1558-0229"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGIR Forum","raw_type":"journal-article"},{"id":"pmh:oai:scholarworks.umass.edu:dissertations_2-2850","is_oa":true,"landing_page_url":"https://scholarworks.umass.edu/dissertations_2/1787","pdf_url":null,"source":{"id":"https://openalex.org/S4306402240","display_name":"ScholarWorks@UMassAmherst (University of Massachusetts Amherst)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I24603500","host_organization_name":"University of Massachusetts Amherst","host_organization_lineage":["https://openalex.org/I24603500"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"doi:10.7275/15241368","is_oa":true,"landing_page_url":"https://doi.org/10.7275/15241368","pdf_url":null,"source":{"id":"https://openalex.org/S7407053130","display_name":"University of Massachusetts (UMass) Amherst","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"pmh:oai:scholarworks.umass.edu:dissertations_2-2850","is_oa":true,"landing_page_url":"https://scholarworks.umass.edu/dissertations_2/1787","pdf_url":null,"source":{"id":"https://openalex.org/S4306402240","display_name":"ScholarWorks@UMassAmherst (University of Massachusetts Amherst)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I24603500","host_organization_name":"University of Massachusetts Amherst","host_organization_lineage":["https://openalex.org/I24603500"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":172,"referenced_works":["https://openalex.org/W36903255","https://openalex.org/W281665770","https://openalex.org/W296439881","https://openalex.org/W299870200","https://openalex.org/W658020064","https://openalex.org/W1482214997","https://openalex.org/W1498436455","https://openalex.org/W1513154910","https://openalex.org/W1515865104","https://openalex.org/W1530210183","https://openalex.org/W1532325895","https://openalex.org/W1602206323","https://openalex.org/W1602667807","https://openalex.org/W1788181460","https://openalex.org/W1964348731","https://openalex.org/W1965972569","https://openalex.org/W1966093341","https://openalex.org/W1966443646","https://openalex.org/W1970242704","https://openalex.org/W1973289172","https://openalex.org/W1973435495","https://openalex.org/W1975128126","https://openalex.org/W1979459060","https://openalex.org/W1982858363","https://openalex.org/W1990589796","https://openalex.org/W1990838106","https://openalex.org/W1992113527","https://openalex.org/W1994389483","https://openalex.org/W1997149618","https://openalex.org/W2001619934","https://openalex.org/W2002388301","https://openalex.org/W2021503317","https://openalex.org/W2024463435","https://openalex.org/W2026953311","https://openalex.org/W2027731328","https://openalex.org/W2028521145","https://openalex.org/W2028988057","https://openalex.org/W2030144199","https://openalex.org/W2047221353","https://openalex.org/W2051025610","https://openalex.org/W2052088591","https://openalex.org/W2055981215","https://openalex.org/W2057028302","https://openalex.org/W2068297964","https://openalex.org/W2068902033","https://openalex.org/W2068905009","https://openalex.org/W2069870183","https://openalex.org/W2079168273","https://openalex.org/W2079370602","https://openalex.org/W2087131461","https://openalex.org/W2087818911","https://openalex.org/W2088822034","https://openalex.org/W2093390569","https://openalex.org/W2099471712","https://openalex.org/W2100495367","https://openalex.org/W2105157020","https://openalex.org/W2106837051","https://openalex.org/W2108279663","https://openalex.org/W2114079787","https://openalex.org/W2115584760","https://openalex.org/W2117473841","https://openalex.org/W2125031621","https://openalex.org/W2128739123","https://openalex.org/W2128892113","https://openalex.org/W2130520240","https://openalex.org/W2131462252","https://openalex.org/W2135046866","https://openalex.org/W2136189984","https://openalex.org/W2136542423","https://openalex.org/W2137245235","https://openalex.org/W2138204974","https://openalex.org/W2140310134","https://openalex.org/W2143331230","https://openalex.org/W2146907956","https://openalex.org/W2147152072","https://openalex.org/W2148212498","https://openalex.org/W2153579005","https://openalex.org/W2155106456","https://openalex.org/W2156037541","https://openalex.org/W2156834477","https://openalex.org/W2157881433","https://openalex.org/W2159665776","https://openalex.org/W2159859920","https://openalex.org/W2159981039","https://openalex.org/W2160001241","https://openalex.org/W2163382007","https://openalex.org/W2164547069","https://openalex.org/W2165612380","https://openalex.org/W2169213601","https://openalex.org/W2173213060","https://openalex.org/W2186845332","https://openalex.org/W2219888463","https://openalex.org/W2250434988","https://openalex.org/W2250539671","https://openalex.org/W2250799045","https://openalex.org/W2252143362","https://openalex.org/W2270593706","https://openalex.org/W2294161614","https://openalex.org/W2339829457","https://openalex.org/W2405884322","https://openalex.org/W2418896762","https://openalex.org/W2468421329","https://openalex.org/W2484648951","https://openalex.org/W2499720122","https://openalex.org/W2508504774","https://openalex.org/W2510769428","https://openalex.org/W2513853793","https://openalex.org/W2515351093","https://openalex.org/W2515565210","https://openalex.org/W2536015822","https://openalex.org/W2537515450","https://openalex.org/W2539104130","https://openalex.org/W2539671052","https://openalex.org/W2604436559","https://openalex.org/W2605110487","https://openalex.org/W2605246672","https://openalex.org/W2610935556","https://openalex.org/W2613589950","https://openalex.org/W2648699835","https://openalex.org/W2725049817","https://openalex.org/W2737088691","https://openalex.org/W2740070748","https://openalex.org/W2740635163","https://openalex.org/W2740851615","https://openalex.org/W2755600468","https://openalex.org/W2758859575","https://openalex.org/W2759472822","https://openalex.org/W2769041395","https://openalex.org/W2771567839","https://openalex.org/W2774991067","https://openalex.org/W2783640434","https://openalex.org/W2798483934","https://openalex.org/W2798631888","https://openalex.org/W2798666412","https://openalex.org/W2798972759","https://openalex.org/W2808562776","https://openalex.org/W2809897079","https://openalex.org/W2810075950","https://openalex.org/W2810410758","https://openalex.org/W2884789176","https://openalex.org/W2886987881","https://openalex.org/W2887994002","https://openalex.org/W2889757348","https://openalex.org/W2897754576","https://openalex.org/W2898076813","https://openalex.org/W2900464008","https://openalex.org/W2909544278","https://openalex.org/W2911988918","https://openalex.org/W2922386288","https://openalex.org/W2929292817","https://openalex.org/W2942607211","https://openalex.org/W2945164631","https://openalex.org/W2954956826","https://openalex.org/W2955732934","https://openalex.org/W2962739339","https://openalex.org/W2963157366","https://openalex.org/W2963341956","https://openalex.org/W2963403868","https://openalex.org/W2964121744","https://openalex.org/W2974397365","https://openalex.org/W3002414756","https://openalex.org/W3017330897","https://openalex.org/W3098649723","https://openalex.org/W3099505889","https://openalex.org/W3099984837","https://openalex.org/W3101366597","https://openalex.org/W3101622805","https://openalex.org/W3104674875","https://openalex.org/W3105096762","https://openalex.org/W3105122291","https://openalex.org/W3105690102","https://openalex.org/W3207342693"],"related_works":["https://openalex.org/W2280422768","https://openalex.org/W3143197806","https://openalex.org/W4252555497","https://openalex.org/W3121175838","https://openalex.org/W3016293053","https://openalex.org/W2401723157","https://openalex.org/W2952904874","https://openalex.org/W324626582","https://openalex.org/W4389302559","https://openalex.org/W1690653314"],"abstract_inverted_index":{"Recent":[0],"developments":[1],"of":[2,41,75,114,156,238,330],"machine":[3],"learning":[4,92,174,380],"models,":[5,125,217,253,296],"and":[6,23,78,116,141,164,193,265,294,307,328,333,371,382,389],"in":[7,101,207,268,302,373],"particular":[8],"deep":[9,180],"neural":[10,48,69,106,181,231],"networks,":[11],"have":[12],"yielded":[13],"significant":[14],"improvements":[15],"on":[16,110,179,283],"several":[17,257],"computer":[18],"vision,":[19],"natural":[20],"language":[21],"processing,":[22],"speech":[24],"recognition":[25],"tasks.":[26],"Progress":[27],"with":[28,359],"information":[29,55,258,304,318,365,387],"retrieval":[30,56,201,216,225,240,262,293,348,388],"(IR)":[31],"tasks":[32,77],"has":[33],"been":[34],"slower,":[35],"however,":[36],"due":[37],"to":[38,175,198,275,362],"the":[39,88,146,208,236,269,279,290,298,311,317,322,377],"lack":[40],"large-scale":[42,394],"training":[43,87,98,118,367,395],"data":[44,119,396],"as":[45,47,220,222,261,297],"well":[46],"network":[49,70,232],"models":[50,107,133,384],"specifically":[51],"designed":[52],"for":[53,72,86,139,153,191,234,242,278,366,385],"effective":[54,383],"[9].":[57],"In":[58,342],"this":[59,374],"dissertation,":[60],"we":[61,345],"address":[62],"these":[63],"two":[64,299],"issues":[65],"by":[66,202,248],"introducing":[67],"task-specific":[68],"architectures":[71],"a":[73,111,154,172,188,230,239,243,347],"set":[74,113,155],"IR":[76,157],"proposing":[79],"novel":[80],"unsupervised":[81],"or":[82,126],"weakly":[83],"supervised":[84],"solutions":[85,93,370],"models.":[89,226],"The":[90],"proposed":[91],"do":[94],"not":[95,398],"require":[96],"labeled":[97],"data.":[99],"Instead,":[100],"our":[102,254],"weak":[103],"supervision":[104],"approach,":[105],"are":[108],"trained":[109,352],"large":[112],"noisy":[115],"biased":[117],"obtained":[120],"from":[121],"external":[122],"resources,":[123],"existing":[124,249],"heuristics.":[127],"We":[128,143,169,227,287],"first":[129],"introduce":[130,346],"relevance-based":[131],"embedding":[132],"[3]":[134],"that":[135,145,350],"learn":[136],"distributed":[137],"representations":[138,148],"words":[140],"queries.":[142],"show":[144],"learned":[147,209],"can":[149],"be":[150],"effectively":[151],"employed":[152],"tasks,":[158,391],"including":[159],"query":[160,165,245,250],"expansion,":[161],"pseudo-relevance":[162],"feedback,":[163],"classification":[166],"[1,":[167],"2].":[168],"further":[170],"propose":[171,229],"standalone":[173],"rank":[176],"model":[177,186,213,241,349],"based":[178],"networks":[182],"[5,":[183],"8].":[184],"Our":[185,212,369],"learns":[187],"sparse":[189],"representation":[190],"queries":[192],"documents.":[194,272],"This":[195,273],"enables":[196],"us":[197],"perform":[199],"efficient":[200,381],"constructing":[203],"an":[204],"inverted":[205],"index":[206],"semantic":[210],"space.":[211],"outperforms":[214],"state-of-the-art":[215,276],"while":[218],"performing":[219],"efficiently":[221],"term":[223,266],"matching":[224],"additionally":[228],"framework":[233,255],"predicting":[235],"performance":[237,251,280],"given":[244],"[7].":[246],"Inspired":[247],"prediction":[252,281],"integrates":[256],"sources,":[259],"such":[260],"score":[263],"distribution":[264,267],"top":[270],"retrieved":[271],"leads":[274],"results":[277],"task":[282],"various":[284,386],"standard":[285],"collections.":[286],"finally":[288],"bridge":[289],"gap":[291],"between":[292],"recommendation":[295,308,357],"key":[300],"components":[301],"most":[303],"systems.":[305],"Search":[306],"often":[309],"share":[310],"same":[312],"goal:":[313],"helping":[314],"people":[315],"get":[316],"they":[319],"need":[320,361],"at":[321],"right":[323],"time.":[324],"Therefore,":[325],"joint":[326],"modeling":[327],"optimization":[329],"search":[331],"engines":[332],"recommender":[334],"systems":[335,340],"could":[336],"potentially":[337],"benefit":[338],"both":[339],"[4].":[341],"more":[343],"detail,":[344],"is":[351,397],"using":[353],"user-item":[354],"interaction":[355],"(e.g.,":[356],"data),":[358],"no":[360],"query-document":[363],"relevance":[364],"[6].":[368],"findings":[372],"dissertation":[375],"smooth":[376],"path":[378],"towards":[379],"related":[390],"especially":[392],"when":[393],"available.":[399]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}