{"id":"https://openalex.org/W2251558607","doi":"https://doi.org/10.18653/v1/k15-1013","title":"Detecting Semantically Equivalent Questions in Online User Forums","display_name":"Detecting Semantically Equivalent Questions in Online User Forums","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2251558607","doi":"https://doi.org/10.18653/v1/k15-1013","mag":"2251558607"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k15-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1013","pdf_url":"https://doi.org/10.18653/v1/k15-1013","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/k15-1013","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025586707","display_name":"Dasha Bogdanova","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dasha Bogdanova","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110645770","display_name":"C\u00edcero dos Santos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cicero dos Santos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000683692","display_name":"Luciano Barbosa","orcid":"https://orcid.org/0000-0002-6858-4773"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luciano Barbosa","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006347550","display_name":"Bianca Zadrozny","orcid":"https://orcid.org/0000-0002-7260-2057"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bianca Zadrozny","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025586707"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.4918,"has_fulltext":false,"cited_by_count":74,"citation_normalized_percentile":{"value":0.97965837,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"123","last_page":"131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9987000226974487,"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/T12478","display_name":"Wikis in Education and Collaboration","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/3315","display_name":"Communication"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7143877744674683},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.45828738808631897},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4416210353374481}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143877744674683},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.45828738808631897},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4416210353374481}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/k15-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1013","pdf_url":"https://doi.org/10.18653/v1/k15-1013","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/k15-1013","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k15-1013","pdf_url":"https://doi.org/10.18653/v1/k15-1013","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251558607.pdf","grobid_xml":"https://content.openalex.org/works/W2251558607.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W1493309689","https://openalex.org/W1614298861","https://openalex.org/W1826419936","https://openalex.org/W1832693441","https://openalex.org/W2101609803","https://openalex.org/W2103305545","https://openalex.org/W2117671523","https://openalex.org/W2119821739","https://openalex.org/W2132069633","https://openalex.org/W2145349611","https://openalex.org/W2152180407","https://openalex.org/W2153635508","https://openalex.org/W2170738476","https://openalex.org/W2250682757","https://openalex.org/W2251143283","https://openalex.org/W2252215182"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2350741829","https://openalex.org/W2530322880","https://openalex.org/W1596801655","https://openalex.org/W2359140296"],"abstract_inverted_index":{"Two":[0],"questions":[1,31,79],"asking":[2],"the":[3,93,99,102,118],"same":[4],"thing":[5],"could":[6],"be":[7],"too":[8],"different":[9,47],"in":[10,32],"terms":[11],"of":[12,41,78,101,110],"vocabulary":[13],"and":[14,80,106],"syntactic":[15],"structure,":[16],"which":[17],"makes":[18],"identifying":[19],"their":[20],"semantic":[21],"equivalence":[22],"challenging.":[23],"This":[24],"study":[25],"aims":[26],"to":[27],"detect":[28],"semantically":[29],"equivalent":[30],"online":[33],"user":[34],"forums.":[35,50],"We":[36,51,87],"perform":[37],"an":[38],"extensive":[39],"number":[40],"experiments":[42],"using":[43,83],"data":[44],"from":[45],"two":[46],"Stack":[48],"Exchange":[49],"compare":[52],"standard":[53],"machine":[54],"learning":[55],"methods":[56],"such":[57],"as":[58],"Support":[59],"Vector":[60],"Machines":[61],"(SVM)":[62],"with":[63,96,122,130],"a":[64,84],"convolutional":[65,119],"neural":[66,120],"network":[67,121],"(CNN).":[68],"The":[69],"proposed":[70],"CNN":[71],"generates":[72],"distributed":[73],"vector":[74],"representations":[75],"for":[76],"pairs":[77],"scores":[81],"them":[82],"similarity":[85],"metric.":[86],"evaluate":[88,107],"in-domain":[89,123],"word":[90,124],"embeddings":[91,125],"versus":[92],"ones":[94],"trained":[95],"Wikipedia,":[97],"estimate":[98],"impact":[100],"training":[103,132],"set":[104],"size,":[105],"some":[108],"aspects":[109],"domain":[111],"adaptation.":[112],"Our":[113],"experimental":[114],"results":[115],"show":[116],"that":[117],"achieves":[126],"high":[127],"performance":[128],"even":[129],"limited":[131],"data.":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}