{"id":"https://openalex.org/W3151320743","doi":"https://doi.org/10.1109/cist49399.2021.9357187","title":"Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies","display_name":"Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies","publication_year":2020,"publication_date":"2020-06-05","ids":{"openalex":"https://openalex.org/W3151320743","doi":"https://doi.org/10.1109/cist49399.2021.9357187","mag":"3151320743"},"language":"en","primary_location":{"id":"doi:10.1109/cist49399.2021.9357187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cist49399.2021.9357187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2201.04810","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027242190","display_name":"Jeena Kleenankandy","orcid":"https://orcid.org/0000-0002-5831-1982"},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Jeena Kleenankandy","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Calicut, Kerala, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Calicut, Kerala, India","institution_ids":["https://openalex.org/I114845381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019106542","display_name":"K. A. Abdul Nazeer","orcid":"https://orcid.org/0000-0002-0527-9026"},"institutions":[{"id":"https://openalex.org/I114845381","display_name":"National Institute of Technology Calicut","ror":"https://ror.org/03yyd7552","country_code":"IN","type":"education","lineage":["https://openalex.org/I114845381"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K A Abdul Nazeer","raw_affiliation_strings":["Department of Computer Science and Engineering, National Institute of Technology, Calicut, Kerala, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, National Institute of Technology, Calicut, Kerala, India","institution_ids":["https://openalex.org/I114845381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5027242190"],"corresponding_institution_ids":["https://openalex.org/I114845381"],"apc_list":null,"apc_paid":null,"fwci":0.2743,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6703017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2010","issue":null,"first_page":"372","last_page":"377"},"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":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/T13629","display_name":"Text Readability and Simplification","score":0.9965000152587891,"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/recurrent-neural-network","display_name":"Recurrent neural network","score":0.8644148111343384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8096084594726562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7181652784347534},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7134138345718384},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6913201212882996},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.6604076623916626},{"id":"https://openalex.org/keywords/textual-entailment","display_name":"Textual entailment","score":0.5649223327636719},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.5433699488639832},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.530127227306366},{"id":"https://openalex.org/keywords/abstract-syntax-tree","display_name":"Abstract syntax tree","score":0.5154286623001099},{"id":"https://openalex.org/keywords/logical-consequence","display_name":"Logical consequence","score":0.48954448103904724},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.45747068524360657},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.42689990997314453},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17443346977233887},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10647416114807129}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.8644148111343384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8096084594726562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7181652784347534},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7134138345718384},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6913201212882996},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.6604076623916626},{"id":"https://openalex.org/C95318506","wikidata":"https://www.wikidata.org/wiki/Q6588467","display_name":"Textual entailment","level":3,"score":0.5649223327636719},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.5433699488639832},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.530127227306366},{"id":"https://openalex.org/C58646249","wikidata":"https://www.wikidata.org/wiki/Q127380","display_name":"Abstract syntax tree","level":3,"score":0.5154286623001099},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.48954448103904724},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.45747068524360657},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.42689990997314453},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17443346977233887},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10647416114807129},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cist49399.2021.9357187","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cist49399.2021.9357187","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 6th IEEE Congress on Information Science and Technology (CiSt)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2201.04810","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.04810","pdf_url":"https://arxiv.org/pdf/2201.04810","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2201.04810","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2201.04810","pdf_url":"https://arxiv.org/pdf/2201.04810","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1423339008","https://openalex.org/W1832693441","https://openalex.org/W1889268436","https://openalex.org/W2094061585","https://openalex.org/W2123442489","https://openalex.org/W2133280805","https://openalex.org/W2149557440","https://openalex.org/W2250539671","https://openalex.org/W2250790822","https://openalex.org/W2250861254","https://openalex.org/W2251124635","https://openalex.org/W2251939518","https://openalex.org/W2252128625","https://openalex.org/W2571842322","https://openalex.org/W2773143256","https://openalex.org/W2789946045","https://openalex.org/W2790662531","https://openalex.org/W2891632051","https://openalex.org/W2914767245","https://openalex.org/W2942173686","https://openalex.org/W2954910303","https://openalex.org/W2962932173","https://openalex.org/W2963355447","https://openalex.org/W2963451457","https://openalex.org/W2963580443","https://openalex.org/W2964046661","https://openalex.org/W2969068412","https://openalex.org/W2972073841","https://openalex.org/W2972551450","https://openalex.org/W3003718262","https://openalex.org/W3005875885","https://openalex.org/W6628124331","https://openalex.org/W6639364127","https://openalex.org/W6679915538","https://openalex.org/W6691303741","https://openalex.org/W6691459498","https://openalex.org/W6730230071","https://openalex.org/W6732124536","https://openalex.org/W6732986347","https://openalex.org/W6745360642"],"related_works":["https://openalex.org/W2169644218","https://openalex.org/W12963412","https://openalex.org/W2250460949","https://openalex.org/W3158371345","https://openalex.org/W3141423438","https://openalex.org/W2071098659","https://openalex.org/W2627035043","https://openalex.org/W4385571113","https://openalex.org/W2937401546","https://openalex.org/W3030695269"],"abstract_inverted_index":{"Recursive":[0],"neural":[1],"networks":[2],"(Tree-RNNs)":[3],"based":[4],"on":[5,61],"dependency":[6,57],"trees":[7],"are":[8,119],"ubiquitous":[9],"in":[10,55,71,90],"modeling":[11],"sentence":[12,72],"meanings":[13],"as":[14],"they":[15],"effectively":[16],"capture":[17],"semantic":[18,62],"relationships":[19],"between":[20,110],"non-neighborhood":[21],"words.":[22],"However,":[23],"recognizing":[24,67],"semantically":[25],"dissimilar":[26],"sentences":[27],"with":[28],"the":[29,50,56,94,98,111],"same":[30],"words":[31],"and":[32,66,106,116],"syntax":[33],"is":[34],"still":[35],"a":[36,87],"challenge":[37],"to":[38,45],"Tree-RNNs.":[39],"This":[40],"work":[41],"proposes":[42],"an":[43],"improvement":[44,89],"Dependency":[46],"Tree-RNN":[47],"(DT-RNN)":[48],"using":[49,74],"grammatical":[51],"relationship":[52],"type":[53],"identified":[54],"parse.":[58],"Our":[59],"experiments":[60],"relatedness":[63],"scoring":[64],"(SRS)":[65],"textual":[68],"entailment":[69],"(RTE)":[70],"pairs":[73],"SICK":[75],"(Sentence":[76],"Involving":[77],"Compositional":[78],"Knowledge)":[79],"dataset":[80],"show":[81,103],"encouraging":[82],"results.":[83],"The":[84,101],"model":[85],"achieved":[86],"2%":[88],"classification":[91],"accuracy":[92],"for":[93],"RTE":[95],"task":[96],"over":[97],"DT-RNN":[99],"model.":[100],"results":[102],"that":[104],"Pearson's":[105],"Spearman's":[107],"correlation":[108],"measures":[109],"model's":[112],"predicted":[113],"similarity":[114],"scores":[115],"human":[117],"ratings":[118],"higher":[120],"than":[121],"those":[122],"of":[123],"standard":[124],"DT-RNNs.":[125]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
