{"id":"https://openalex.org/W3203876541","doi":"https://doi.org/10.1145/3478905.3478958","title":"Short text similarity computation method based on feature expansion and Siamese network","display_name":"Short text similarity computation method based on feature expansion and Siamese network","publication_year":2021,"publication_date":"2021-07-23","ids":{"openalex":"https://openalex.org/W3203876541","doi":"https://doi.org/10.1145/3478905.3478958","mag":"3203876541"},"language":"en","primary_location":{"id":"doi:10.1145/3478905.3478958","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3478905.3478958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Data Science and Information Technology","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/A5075894222","display_name":"Xinyuan Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinyuan Niu","raw_affiliation_strings":["Tianjin University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041936738","display_name":"Wenguang Zheng","orcid":"https://orcid.org/0000-0003-0474-6611"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenguang Zheng","raw_affiliation_strings":["Tianjin University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082799071","display_name":"Yingyuan Xiao","orcid":"https://orcid.org/0000-0002-5711-8638"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingyuan Xiao","raw_affiliation_strings":["Tianjin University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Tianjin University of Technology, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100391116","display_name":"Qian Wang","orcid":"https://orcid.org/0000-0002-8967-8525"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian Wang","raw_affiliation_strings":["Hebei Jiaotong Vocational and Technical College Navigational Branch, China"],"affiliations":[{"raw_affiliation_string":"Hebei Jiaotong Vocational and Technical College Navigational Branch, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5075894222"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":null,"apc_paid":null,"fwci":0.2719,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63730686,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"268","last_page":"272"},"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9976000189781189,"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":0.7533305883407593},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7516547441482544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7052752375602722},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6836596131324768},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.6522163152694702},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5875269174575806},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5578048229217529},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5415336489677429},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4868411421775818},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4733474552631378},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.2564104497432709},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24316895008087158},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11466142535209656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7533305883407593},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7516547441482544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7052752375602722},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6836596131324768},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.6522163152694702},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5875269174575806},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5578048229217529},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5415336489677429},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4868411421775818},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4733474552631378},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.2564104497432709},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24316895008087158},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11466142535209656},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3478905.3478958","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3478905.3478958","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th International Conference on Data Science and Information Technology","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":18,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1879966306","https://openalex.org/W2088221834","https://openalex.org/W2111369166","https://openalex.org/W2127928557","https://openalex.org/W2167752857","https://openalex.org/W2211192759","https://openalex.org/W2508865106","https://openalex.org/W2800106282","https://openalex.org/W2909646526","https://openalex.org/W2913323467","https://openalex.org/W2914767245","https://openalex.org/W2940421249","https://openalex.org/W2945260058","https://openalex.org/W3045828162","https://openalex.org/W3088934142","https://openalex.org/W3119125170","https://openalex.org/W4205526653"],"related_works":["https://openalex.org/W3107650560","https://openalex.org/W3126382579","https://openalex.org/W4317422773","https://openalex.org/W4315588616","https://openalex.org/W2810542905","https://openalex.org/W3123667230","https://openalex.org/W4243064001","https://openalex.org/W2129350855","https://openalex.org/W2888805565","https://openalex.org/W3096554474"],"abstract_inverted_index":{"Text":[0],"similarity":[1,16,35,66,121],"computation":[2,17,36,67],"issues":[3],"is":[4,18,41,85],"a":[5,19,43,63,78,92],"widely":[6],"studied":[7],"problem":[8],"in":[9],"natural":[10],"language":[11],"processing":[12],"(NLP).":[13],"Short":[14],"text":[15,34,45,65],"new":[20],"and":[21,52,73,113,161],"more":[22],"challenging":[23],"problem,":[24],"which":[25,106],"cannot":[26],"be":[27,56,127],"effectively":[28],"solved":[29],"by":[30,100,129],"using":[31,101],"previous":[32],"regular":[33],"approach.":[37],"The":[38],"main":[39],"reason":[40],"that,":[42,145],"short":[44,64,93,124],"generally":[46],"contains":[47,107],"limited":[48],"number":[49],"of":[50,91,122,138,151],"words":[51],"fewer":[53],"features":[54,90,97],"can":[55,126],"extracted.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"method":[68,157],"based":[69,83,146],"on":[70,147],"feature":[71,136],"expansion":[72],"Siamese":[74,102],"neural":[75,103,110],"network.":[76],"Firstly,":[77],"latent":[79],"Dirichlet":[80],"allocation":[81],"(LDA)":[82],"model":[84,105],"constructed":[86],"to":[87],"expand":[88],"the":[89,120,131,148],"text.":[94],"Then,":[95],"deep":[96],"are":[98],"extracted":[99],"networks":[104,111],"both":[108],"convolutional":[109],"(CNN)":[112],"Bi-directional":[114],"long":[115],"short-term":[116],"memory":[117],"(BiLSTM).":[118],"Finally,":[119],"two":[123,140],"texts":[125],"achieved":[128],"computing":[130],"Manhattan":[132],"distance":[133],"between":[134],"generated":[135],"vectors":[137],"these":[139],"texts.":[141],"Experimental":[142],"results":[143],"show":[144],"data":[149],"set":[150],"Ant":[152],"Financial":[153],"NLP":[154],"Challenge,":[155],"our":[156],"achieves":[158],"higher":[159],"accuracy":[160],"F1":[162],"score.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}