{"id":"https://openalex.org/W2809897079","doi":"https://doi.org/10.1145/3209978.3210041","title":"Neural Query Performance Prediction using Weak Supervision from Multiple Signals","display_name":"Neural Query Performance Prediction using Weak Supervision from Multiple Signals","publication_year":2018,"publication_date":"2018-06-27","ids":{"openalex":"https://openalex.org/W2809897079","doi":"https://doi.org/10.1145/3209978.3210041","mag":"2809897079"},"language":"en","primary_location":{"id":"doi:10.1145/3209978.3210041","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"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, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105659698","display_name":"W. Bruce Croft","orcid":"https://orcid.org/0000-0003-2391-9629"},"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":false,"raw_author_name":"W. Bruce Croft","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, MA, USA"],"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, MA, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070937840","display_name":"J. Shane Culpepper","orcid":"https://orcid.org/0000-0002-1902-9087"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"J. Shane Culpepper","raw_affiliation_strings":["RMIT University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101457713"],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":21.3852,"has_fulltext":false,"cited_by_count":96,"citation_normalized_percentile":{"value":0.99357247,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"105","last_page":"114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9990000128746033,"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":0.9990000128746033,"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/T11106","display_name":"Data Management and Algorithms","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9958000183105469,"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":0.8096500039100647},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7272042036056519},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5754146575927734},{"id":"https://openalex.org/keywords/performance-prediction","display_name":"Performance prediction","score":0.5450742840766907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5403151512145996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5353569984436035},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5296813249588013},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.513179361820221},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5092705488204956},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5043202638626099},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.476953387260437},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39007529616355896},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.2376585304737091}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8096500039100647},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7272042036056519},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5754146575927734},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.5450742840766907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5403151512145996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5353569984436035},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5296813249588013},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.513179361820221},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5092705488204956},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5043202638626099},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.476953387260437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39007529616355896},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2376585304737091},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3209978.3210041","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3209978.3210041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:figshare.com:article/27583560","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/27583560","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W125377052","https://openalex.org/W146156986","https://openalex.org/W1498436455","https://openalex.org/W1513154910","https://openalex.org/W1554728883","https://openalex.org/W1602206323","https://openalex.org/W1685426458","https://openalex.org/W1964348731","https://openalex.org/W1970242704","https://openalex.org/W1972594981","https://openalex.org/W1982858363","https://openalex.org/W1990838106","https://openalex.org/W1999322447","https://openalex.org/W2009077327","https://openalex.org/W2024463435","https://openalex.org/W2028521145","https://openalex.org/W2051025610","https://openalex.org/W2057028302","https://openalex.org/W2068902033","https://openalex.org/W2069870183","https://openalex.org/W2070740689","https://openalex.org/W2079168273","https://openalex.org/W2079370602","https://openalex.org/W2087131461","https://openalex.org/W2087408015","https://openalex.org/W2087818911","https://openalex.org/W2093390569","https://openalex.org/W2102046030","https://openalex.org/W2148212498","https://openalex.org/W2153579005","https://openalex.org/W2159665776","https://openalex.org/W2159981039","https://openalex.org/W2164547069","https://openalex.org/W2169213601","https://openalex.org/W2250539671","https://openalex.org/W2250799045","https://openalex.org/W2294161614","https://openalex.org/W2418896762","https://openalex.org/W2469056188","https://openalex.org/W2531355666","https://openalex.org/W2539104130","https://openalex.org/W2610935556","https://openalex.org/W2613589950","https://openalex.org/W2740635163","https://openalex.org/W2755600468","https://openalex.org/W2758859575","https://openalex.org/W2759472822","https://openalex.org/W2765482949","https://openalex.org/W2768459074","https://openalex.org/W2771567839","https://openalex.org/W2782730635","https://openalex.org/W2810075950","https://openalex.org/W2810410758","https://openalex.org/W2964121744","https://openalex.org/W3101622805","https://openalex.org/W3138850522","https://openalex.org/W3212575067","https://openalex.org/W4206765718","https://openalex.org/W4246858749","https://openalex.org/W4312943126"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W1521968289","https://openalex.org/W2782041652","https://openalex.org/W2952088488","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2792147139","https://openalex.org/W2612657834","https://openalex.org/W2005039966","https://openalex.org/W1987310671"],"abstract_inverted_index":{"Predicting":[0],"the":[1,35,44,111,156],"performance":[2,21,60,81,114],"of":[3,72,158],"a":[4,8,12,56,77,91,122],"search":[5],"engine":[6],"for":[7,80],"given":[9],"query":[10,49,59],"is":[11,104],"fundamental":[13],"and":[14,88,138,165],"challenging":[15],"task":[16],"in":[17,26,148,169],"information":[18],"retrieval.":[19],"Accurate":[20],"predictors":[22,115],"can":[23],"be":[24],"used":[25],"various":[27],"ways,":[28],"such":[29],"as":[30],"triggering":[31],"an":[32,105],"action,":[33],"choosing":[34],"most":[36],"effective":[37,125],"ranking":[38],"function":[39],"per":[40],"query,":[41],"or":[42],"selecting":[43],"best":[45],"variant":[46],"from":[47],"multiple":[48,73,99],"formulations.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,153],"propose":[55,121],"general":[57],"end-to-end":[58],"prediction":[61,92],"framework":[62,70],"based":[63],"on":[64,135],"neural":[65],"networks,":[66],"called":[67],"NeuralQPP.":[68],"Our":[69,133],"consists":[71],"components,":[74],"each":[75,159,161],"learning":[76,107],"representation":[78],"suitable":[79],"prediction.":[82],"These":[83],"representations":[84],"are":[85],"then":[86],"aggregated":[87],"fed":[89],"into":[90],"sub-network.":[93],"We":[94,119],"train":[95],"our":[96,131,170],"models":[97],"with":[98],"weak":[100,117,162],"supervision":[101,163],"signals,":[102],"which":[103],"unsupervised":[106,113],"approach":[108],"that":[109,142],"uses":[110],"existing":[112],"using":[116],"labels.":[118],"also":[120],"simple":[123],"yet":[124],"component":[126],"dropout":[127],"technique":[128],"to":[129],"regularize":[130],"model.":[132],"experiments":[134],"four":[136],"newswire":[137],"web":[139],"collections":[140],"demonstrate":[141],"NeuralQPP":[143],"significantly":[144],"outperforms":[145],"state-of-the-art":[146],"baselines,":[147],"nearly":[149],"every":[150],"case.":[151],"Furthermore,":[152],"thoroughly":[154],"analyze":[155],"effectiveness":[157],"component,":[160],"signal,":[164],"all":[166],"resulting":[167],"combinations":[168],"experiments.":[171]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":20},{"year":2018,"cited_by_count":5}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}