{"id":"https://openalex.org/W1991969934","doi":"https://doi.org/10.1145/2644866.2644881","title":"A new sentence similarity assessment measure based on a three-layer sentence representation","display_name":"A new sentence similarity assessment measure based on a three-layer sentence representation","publication_year":2014,"publication_date":"2014-09-16","ids":{"openalex":"https://openalex.org/W1991969934","doi":"https://doi.org/10.1145/2644866.2644881","mag":"1991969934"},"language":"en","primary_location":{"id":"doi:10.1145/2644866.2644881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2644866.2644881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM symposium on Document engineering","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/A5110318692","display_name":"Rafael Ferreira","orcid":null},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Rafael Ferreira","raw_affiliation_strings":["Federal University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042210961","display_name":"Rafael Dueire Lins","orcid":"https://orcid.org/0000-0003-3497-5044"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rafael Dueire Lins","raw_affiliation_strings":["Federal University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103176344","display_name":"Fred Freitas","orcid":"https://orcid.org/0000-0003-0425-6786"},"institutions":[{"id":"https://openalex.org/I25112270","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24","country_code":"BR","type":"education","lineage":["https://openalex.org/I25112270"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Fred Freitas","raw_affiliation_strings":["Federal University of Pernambuco, Recife, Brazil"],"affiliations":[{"raw_affiliation_string":"Federal University of Pernambuco, Recife, Brazil","institution_ids":["https://openalex.org/I25112270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022519243","display_name":"Steven J. Simske","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven J. Simske","raw_affiliation_strings":["Hewlett-Packard Labs, Fort Collins, CO, USA","Hewlett Packard Laboratories, Fort Collins, CO, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Hewlett-Packard Labs, Fort Collins, CO, USA","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"Hewlett Packard Laboratories, Fort Collins, CO, USA#TAB#","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015355059","display_name":"Marcelo Riss","orcid":"https://orcid.org/0000-0002-6361-8145"},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marcelo Riss","raw_affiliation_strings":["Hewlett-Packard Brazil, Porto Alegre, Brazil","Hewlett-Packard Brazil, Porto Alegre, Brazil#TAB#"],"affiliations":[{"raw_affiliation_string":"Hewlett-Packard Brazil, Porto Alegre, Brazil","institution_ids":[]},{"raw_affiliation_string":"Hewlett-Packard Brazil, Porto Alegre, Brazil#TAB#","institution_ids":["https://openalex.org/I1324840837"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5110318692"],"corresponding_institution_ids":["https://openalex.org/I25112270"],"apc_list":null,"apc_paid":null,"fwci":3.6811,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9345188,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"25","last_page":"34"},"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":0.9998000264167786,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9986000061035156,"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":0.8020864725112915},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7865217924118042},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.7237804532051086},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7215460538864136},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.7020041942596436},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5996980667114258},{"id":"https://openalex.org/keywords/machine-translation","display_name":"Machine translation","score":0.5181887745857239},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5161029100418091},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5153952836990356},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4857252240180969},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4733947515487671},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.416459858417511},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.18187230825424194},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.10487926006317139}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8020864725112915},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7865217924118042},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7237804532051086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7215460538864136},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.7020041942596436},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5996980667114258},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.5181887745857239},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5161029100418091},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5153952836990356},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4857252240180969},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4733947515487671},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.416459858417511},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.18187230825424194},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.10487926006317139},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2644866.2644881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2644866.2644881","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM symposium on Document engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7200000286102295,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324901","display_name":"Universidade Federal de Pernambuco","ror":"https://ror.org/047908t24"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W74108380","https://openalex.org/W80463681","https://openalex.org/W105852416","https://openalex.org/W133424112","https://openalex.org/W1495207358","https://openalex.org/W1522842151","https://openalex.org/W1566018662","https://openalex.org/W1970417391","https://openalex.org/W1978264551","https://openalex.org/W1981313612","https://openalex.org/W1984364982","https://openalex.org/W2007407316","https://openalex.org/W2022470916","https://openalex.org/W2033339319","https://openalex.org/W2037414258","https://openalex.org/W2038721957","https://openalex.org/W2045929671","https://openalex.org/W2096672935","https://openalex.org/W2101105183","https://openalex.org/W2121184547","https://openalex.org/W2123457740","https://openalex.org/W2123946480","https://openalex.org/W2136930489","https://openalex.org/W2154652894","https://openalex.org/W2461708070","https://openalex.org/W4235505822","https://openalex.org/W4245787400","https://openalex.org/W6603158870","https://openalex.org/W6682631176"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W1517524280","https://openalex.org/W4323520239","https://openalex.org/W4389760904","https://openalex.org/W4284703357"],"abstract_inverted_index":{"Sentence":[0],"similarity":[1,37,67,97,141],"is":[2,13,62],"used":[3,14],"to":[4,156],"measure":[5,99,143],"the":[6,47,51,54,66,69,72,78,80,84,113,139,146,149,157],"degree":[7,57],"of":[8,42,44,50,58,119,148],"likelihood":[9,59],"between":[10,60,68],"sentences.":[11,73,120],"It":[12],"in":[15,53,71,77],"many":[16],"natural":[17],"language":[18],"applications,":[19],"such":[20],"as":[21,40],"text":[22,26],"summarization,":[23],"information":[24,49],"retrieval,":[25],"categorization,":[27],"and":[28,83,103,116,152],"machine":[29],"translation.":[30],"The":[31,56,121,134],"current":[32],"methods":[33],"for":[34],"assessing":[35],"sentence":[36,96],"represent":[38],"sentences":[39],"vectors":[41],"bag":[43],"words":[45,52,70],"or":[46],"syntactic":[48,115],"sentence.":[55],"phrases":[61],"calculated":[63],"by":[64,160],"composing":[65],"Two":[74],"important":[75],"concerns":[76],"area,":[79],"meaning":[81],"problem":[82],"word":[85],"order,":[86],"are":[87],"not":[88],"handled,":[89],"however.":[90],"This":[91],"paper":[92],"proposes":[93],"a":[94,105,129],"new":[95,122,140],"assessment":[98,142],"that":[100,109,138],"largely":[101],"improves":[102],"refines":[104],"recently":[106],"published":[107],"method":[108,123],"takes":[110],"into":[111],"account":[112],"lexical,":[114],"semantic":[117],"components":[118],"proposed":[124,144],"here":[125],"was":[126],"benchmarked":[127],"using":[128],"publically":[130],"available":[131],"standard":[132],"dataset.":[133],"results":[135,154],"obtained":[136],"show":[137],"outperforms":[145],"state":[147],"art":[150],"systems":[151],"achieve":[153],"comparable":[155],"evaluation":[158],"made":[159],"humans.":[161]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}