{"id":"https://openalex.org/W2767937077","doi":"https://doi.org/10.1145/3132847.3132861","title":"Hybrid BiLSTM-Siamese network for FAQ Assistance","display_name":"Hybrid BiLSTM-Siamese network for FAQ Assistance","publication_year":2017,"publication_date":"2017-11-06","ids":{"openalex":"https://openalex.org/W2767937077","doi":"https://doi.org/10.1145/3132847.3132861","mag":"2767937077"},"language":"en","primary_location":{"id":"doi:10.1145/3132847.3132861","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM 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/A5016004313","display_name":"Prerna Khurana","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Prerna Khurana","raw_affiliation_strings":["TCS Research, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103222366","display_name":"Puneet Agarwal","orcid":"https://orcid.org/0000-0002-0063-5079"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Puneet Agarwal","raw_affiliation_strings":["TCS Research, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038397893","display_name":"Gautam Shroff","orcid":"https://orcid.org/0000-0002-0340-0283"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gautam Shroff","raw_affiliation_strings":["TCS Research, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071894271","display_name":"Lovekesh Vig","orcid":"https://orcid.org/0000-0001-9834-3308"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lovekesh Vig","raw_affiliation_strings":["TCS Research, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"TCS Research, New Delhi, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103083219","display_name":"Ashwin Srinivasan","orcid":"https://orcid.org/0000-0002-4911-0038"},"institutions":[{"id":"https://openalex.org/I74796645","display_name":"Birla Institute of Technology and Science, Pilani","ror":"https://ror.org/001p3jz28","country_code":"IN","type":"education","lineage":["https://openalex.org/I74796645"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashwin Srinivasan","raw_affiliation_strings":["BITS Pilani, Goa, India"],"affiliations":[{"raw_affiliation_string":"BITS Pilani, Goa, India","institution_ids":["https://openalex.org/I74796645"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016004313"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3651,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.86278957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"537","last_page":"545"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9959999918937683,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9902999997138977,"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":0.7556389570236206},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7319516539573669},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6115648150444031},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5182737708091736},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.49270227551460266},{"id":"https://openalex.org/keywords/frequently-asked-questions","display_name":"Frequently asked questions","score":0.4558587670326233},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4489881992340088},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.42303991317749023},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4125160574913025}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7556389570236206},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7319516539573669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6115648150444031},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5182737708091736},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.49270227551460266},{"id":"https://openalex.org/C3018615553","wikidata":"https://www.wikidata.org/wiki/Q189293","display_name":"Frequently asked questions","level":2,"score":0.4558587670326233},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4489881992340088},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.42303991317749023},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4125160574913025},{"id":"https://openalex.org/C509550671","wikidata":"https://www.wikidata.org/wiki/Q126945","display_name":"Medical education","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3132847.3132861","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3132847.3132861","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W1522301498","https://openalex.org/W1646084575","https://openalex.org/W1793121960","https://openalex.org/W1832693441","https://openalex.org/W1836465849","https://openalex.org/W1995562189","https://openalex.org/W2064675550","https://openalex.org/W2157364932","https://openalex.org/W2158899491","https://openalex.org/W2170240176","https://openalex.org/W2170738476","https://openalex.org/W2173361515","https://openalex.org/W2173649752","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2265846598","https://openalex.org/W2508865106","https://openalex.org/W2511678010","https://openalex.org/W2556888587","https://openalex.org/W2579486552","https://openalex.org/W2593390416","https://openalex.org/W2604940557","https://openalex.org/W2739802459","https://openalex.org/W2808434001","https://openalex.org/W2902455138","https://openalex.org/W2949888546","https://openalex.org/W2950133940","https://openalex.org/W2963304263","https://openalex.org/W2964015640","https://openalex.org/W4254181784"],"related_works":["https://openalex.org/W2001939411","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W2169074127","https://openalex.org/W83146503","https://openalex.org/W2163707935","https://openalex.org/W202723009","https://openalex.org/W2145955964","https://openalex.org/W2188612292","https://openalex.org/W4206462905"],"abstract_inverted_index":{"We":[0,121,185,253],"describe":[1],"an":[2,54,140,149],"automated":[3,284],"assistant":[4,285],"for":[5,61,96,144],"answering":[6,18,300],"frequently":[7,48],"asked":[8,49,59],"questions;":[9],"our":[10,83,190,194,261,283,294],"system":[11,103],"has":[12,230],"been":[13,231],"deployed,":[14,232],"and":[15,27,91,243,277],"is":[16,64,80,104,132],"currently":[17],"HR-related":[19],"queries":[20,303],"in":[21,127,139,201,223,227,233,260,265],"two":[22,225],"different":[23],"areas":[24,226],"(leave":[25],"management":[26],"health":[28],"insurance)":[29],"to":[30,45,52,82,155,170,175,297,306],"a":[31,39,47,65,77,123,129,157,207,212,249,304],"large":[32,40],"number":[33],"of":[34,38,57,89,159,181,235,275,282],"users.":[35],"The":[36],"needs":[37],"global":[41],"corporate":[42],"lead":[43],"us":[44],"model":[46],"question":[50,79],"(FAQ)":[51],"be":[53],"equivalence":[55],"class":[56,88],"actually":[58],"questions,":[60],"which":[62,128,145,228],"there":[63],"common":[66],"answer":[67,95],"(certified":[68],"as":[69,238,240],"being":[70],"consistent":[71],"with":[72,93,134,165],"the":[73,87,94,97,102,146,172,178,182,224,280,291],"organization's":[74],"policy).":[75],"When":[76],"new":[78],"posed":[81],"system,":[84],"it":[85,229],"finds":[86],"question,":[90],"responds":[92],"class.":[98],"At":[99],"this":[100],"point,":[101],"either":[105],"correct":[106,108,166],"(gives":[107,112],"answer);":[109,114],"or":[110,115,210],"incorrect":[111],"wrong":[113],"incomplete":[116],"(says":[117],"\"I":[118],"don't":[119],"know'').":[120],"employ":[122],"hybrid":[124,197,262],"deep-learning":[125],"architecture":[126],"BiLSTM-based":[130,136],"classifier":[131,147,208],"combined":[133],"second":[135],"Siamese":[137,173,213],"network":[138,174],"iterative":[141],"manner:":[142],"Questions":[143],"makes":[148],"error":[150],"during":[151],"training":[152],"are":[153,168],"used":[154,169],"generate":[156],"set":[158],"misclassified":[160,183],"question-question":[161,258],"pairs.":[162,184,272],"These,":[163],"along":[164],"pairs,":[167],"train":[171],"drive":[176],"apart":[177],"(hidden)":[179],"representations":[180],"present":[186],"experimental":[187],"results":[188,200,264],"from":[189,279,299],"deployment":[191,281],"showing":[192],"that":[193,256,287],"iteratively":[195],"trained":[196],"network:":[198],"(a)":[199],"better":[202,217,267],"performance":[203,268],"than":[204,218,269],"using":[205,257,270],"just":[206,211],"network,":[209,263],"network;":[214],"(b)":[215],"performs":[216,246],"state-of-the":[219],"art":[220],"sentence":[221],"classifiers":[222],"terms":[234],"both":[236],"accuracy":[237],"well":[239,247],"precision-recall":[241],"tradeoff;":[242],"(c)":[244],"also":[245,254],"on":[248,293],"benchmark":[250],"public":[251],"dataset.":[252],"observe":[255],"pairs":[259],"marginally":[266],"question-to-answer":[271],"Finally,":[273],"estimates":[274],"precision":[276],"recall":[278],"suggest":[286],"we":[288],"can":[289],"expect":[290],"burden":[292],"HR":[295],"department":[296],"drop":[298],"about":[301,307],"6000":[302],"day":[305],"1000.":[308]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}