{"id":"https://openalex.org/W2125444198","doi":"https://doi.org/10.1162/tacl_a_00133","title":"Higher-order Lexical Semantic Models for Non-factoid Answer Reranking","display_name":"Higher-order Lexical Semantic Models for Non-factoid Answer Reranking","publication_year":2015,"publication_date":"2015-12-01","ids":{"openalex":"https://openalex.org/W2125444198","doi":"https://doi.org/10.1162/tacl_a_00133","mag":"2125444198"},"language":"en","primary_location":{"id":"doi:10.1162/tacl_a_00133","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl_a_00133","pdf_url":"http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00133","source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00133","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102739620","display_name":"Daniel Fried","orcid":"https://orcid.org/0000-0002-0454-9792"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Fried","raw_affiliation_strings":["University of Arizona, Tucson, AZ, USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, AZ, USA,","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111404777","display_name":"Peter Jansen","orcid":null},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Jansen","raw_affiliation_strings":["University of Arizona, Tucson, AZ, USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, AZ, USA,","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030626360","display_name":"Gus Hahn-Powell","orcid":"https://orcid.org/0000-0002-7957-3586"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gustave Hahn-Powell","raw_affiliation_strings":["University of Arizona, Tucson, AZ, USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, AZ, USA,","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047699502","display_name":"Mihai Surdeanu","orcid":"https://orcid.org/0000-0001-6956-8030"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mihai Surdeanu","raw_affiliation_strings":["University of Arizona, Tucson, AZ, USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, AZ, USA,","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081635691","display_name":"Peter E. Clark","orcid":"https://orcid.org/0000-0002-8006-7015"},"institutions":[{"id":"https://openalex.org/I4210156221","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Clark","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA, USA,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA, USA,","institution_ids":["https://openalex.org/I4210156221"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.0402,"has_fulltext":true,"cited_by_count":60,"citation_normalized_percentile":{"value":0.98070048,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"3","issue":null,"first_page":"197","last_page":"210"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9995999932289124,"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/T12031","display_name":"Speech and dialogue systems","score":0.9909999966621399,"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":0.8788723945617676},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.755378007888794},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6673244833946228},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.535259485244751},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4899956285953522},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.4737335443496704},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.4513777494430542},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4299159348011017},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3296719789505005},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08350929617881775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8788723945617676},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.755378007888794},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6673244833946228},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.535259485244751},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4899956285953522},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.4737335443496704},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.4513777494430542},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4299159348011017},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3296719789505005},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08350929617881775}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1162/tacl_a_00133","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl_a_00133","pdf_url":"http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00133","source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.715.9943","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.715.9943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/%7Edfried/papers/tacl2015-qa.pdf","raw_type":"text"},{"id":"pmh:oai:doaj.org/article:e3d642c9519f463089afec8cc3b3cba5","is_oa":false,"landing_page_url":"https://doaj.org/article/e3d642c9519f463089afec8cc3b3cba5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Transactions of the Association for Computational Linguistics, Vol 3 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1162/tacl_a_00133","is_oa":true,"landing_page_url":"https://doi.org/10.1162/tacl_a_00133","pdf_url":"http://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00133","source":{"id":"https://openalex.org/S2729999759","display_name":"Transactions of the Association for Computational Linguistics","issn_l":"2307-387X","issn":["2307-387X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions of the Association for Computational Linguistics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8100000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320317052","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2125444198.pdf","grobid_xml":"https://content.openalex.org/works/W2125444198.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1508977358","https://openalex.org/W1532325895","https://openalex.org/W1567554833","https://openalex.org/W1614298861","https://openalex.org/W1756422141","https://openalex.org/W1854214752","https://openalex.org/W1990280147","https://openalex.org/W2006969979","https://openalex.org/W2020280480","https://openalex.org/W2029249040","https://openalex.org/W2055518963","https://openalex.org/W2055955951","https://openalex.org/W2065240770","https://openalex.org/W2068223736","https://openalex.org/W2080635174","https://openalex.org/W2096335387","https://openalex.org/W2099596552","https://openalex.org/W2101210369","https://openalex.org/W2105051853","https://openalex.org/W2112576450","https://openalex.org/W2118091490","https://openalex.org/W2119788759","https://openalex.org/W2123442489","https://openalex.org/W2133299088","https://openalex.org/W2155712036","https://openalex.org/W2156985047","https://openalex.org/W2157110051","https://openalex.org/W2166776180","https://openalex.org/W2169624745","https://openalex.org/W2185606683","https://openalex.org/W2228826686","https://openalex.org/W2250189634","https://openalex.org/W2250249423","https://openalex.org/W2251149243","https://openalex.org/W2251771443","https://openalex.org/W2251803266","https://openalex.org/W2293778248","https://openalex.org/W2431106835","https://openalex.org/W4213009331","https://openalex.org/W4255198209","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2136735429","https://openalex.org/W170547082","https://openalex.org/W2587876411","https://openalex.org/W2358332176","https://openalex.org/W4229950834","https://openalex.org/W4301044568","https://openalex.org/W3204607391","https://openalex.org/W2964413124","https://openalex.org/W4388937922","https://openalex.org/W3113264705"],"abstract_inverted_index":{"Lexical":[0],"semantic":[1,56,73,143,164],"models":[2,25,40,74,165],"provide":[3],"robust":[4],"performance":[5],"for":[6,137,141],"question":[7,59,84],"answering,":[8],"but,":[9],"in":[10,151],"general,":[11],"can":[12],"only":[13],"capitalize":[14],"on":[15],"direct":[16,77,99],"evidence":[17,78],"seen":[18],"during":[19,147],"training.":[20],"For":[21],"example,":[22],"monolingual":[23],"alignment":[24,28,121],"acquire":[26],"term":[27,42,100],"probabilities":[29],"from":[30,44,108],"semi-structured":[31],"data":[32],"such":[33],"as":[34,92],"question-answer":[35],"pairs;":[36],"neural":[37],"network":[38],"language":[39,123],"learn":[41],"embeddings":[43],"unstructured":[45],"text.":[46],"All":[47,150],"this":[48],"knowledge":[49],"is":[50,139],"then":[51],"used":[52],"to":[53,75,79,120,172],"estimate":[54],"the":[55,90,93,142,153,161],"similarity":[57],"between":[58,83],"and":[60,85,122,128],"answer":[61,86],"candidates.":[62],"We":[63,131],"introduce":[64],"a":[65,103],"higher-order":[66,115,155],"formalism":[67],"that":[68,97,114,133,145],"allows":[69],"all":[70],"these":[71],"lexical":[72,163],"chain":[76],"construct":[80],"indirect":[81],"associations":[82],"texts,":[87],"by":[88],"casting":[89],"task":[91],"traversal":[94],"of":[95,105,160,170],"graphs":[96],"encode":[98],"associations.":[101],"Using":[102],"corpus":[104],"10,000":[106],"questions":[107],"Yahoo!":[109],"Answers,":[110],"we":[111],"experimentally":[112],"demonstrate":[113],"methods":[116],"are":[117],"broadly":[118],"applicable":[119],"models,":[124],"across":[125],"both":[126],"word":[127],"syntactic":[129],"representations.":[130],"show":[132],"an":[134],"important":[135],"criterion":[136],"success":[138],"controlling":[140],"drift":[144],"accumulates":[146],"graph":[148],"traversal.":[149],"all,":[152],"proposed":[154],"approach":[156],"improves":[157],"five":[158],"out":[159],"six":[162],"investigated,":[166],"with":[167],"relative":[168],"gains":[169],"up":[171],"+13%":[173],"over":[174],"their":[175],"first-order":[176],"variants.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}