{"id":"https://openalex.org/W2920046378","doi":"https://doi.org/10.1145/3297156.3297223","title":"SENet for Weakly-Supervised Relation Extraction","display_name":"SENet for Weakly-Supervised Relation Extraction","publication_year":2018,"publication_date":"2018-12-08","ids":{"openalex":"https://openalex.org/W2920046378","doi":"https://doi.org/10.1145/3297156.3297223","mag":"2920046378"},"language":"en","primary_location":{"id":"doi:10.1145/3297156.3297223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297156.3297223","pdf_url":null,"source":{"id":"https://openalex.org/S4306523626","display_name":"Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence","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/A5101686414","display_name":"Jiashu Liu","orcid":"https://orcid.org/0000-0003-3043-1496"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiashu Liu","raw_affiliation_strings":["PRIS, Beijing University of Posts and Telecommunications, Beijing"],"affiliations":[{"raw_affiliation_string":"PRIS, Beijing University of Posts and Telecommunications, Beijing","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100658510","display_name":"Guang Chen","orcid":"https://orcid.org/0000-0003-0633-4266"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guang Chen","raw_affiliation_strings":["PRIS, Beijing University of Posts and Telecommunications, Beijing"],"affiliations":[{"raw_affiliation_string":"PRIS, Beijing University of Posts and Telecommunications, Beijing","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100445470","display_name":"Jun Guo","orcid":"https://orcid.org/0000-0001-9045-1339"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Guo","raw_affiliation_strings":["PRIS, Beijing University of Posts and Telecommunications, Beijing"],"affiliations":[{"raw_affiliation_string":"PRIS, Beijing University of Posts and Telecommunications, Beijing","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101686414"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.22495994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"511","last_page":"515"},"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":0.784412145614624},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.7727195024490356},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7563065886497498},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7029728889465332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6564100980758667},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6221096515655518},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5758281946182251},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48935216665267944},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.47615572810173035},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.4751324951648712},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.47409138083457947},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4623182415962219},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4261772334575653},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42473679780960083},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4182612895965576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4134785532951355},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3582252562046051},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3374248147010803},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2025485634803772}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.784412145614624},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7727195024490356},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7563065886497498},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7029728889465332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6564100980758667},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6221096515655518},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5758281946182251},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48935216665267944},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.47615572810173035},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.4751324951648712},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.47409138083457947},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4623182415962219},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4261772334575653},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42473679780960083},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4182612895965576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4134785532951355},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3582252562046051},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3374248147010803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2025485634803772},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3297156.3297223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3297156.3297223","pdf_url":null,"source":{"id":"https://openalex.org/S4306523626","display_name":"Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321470","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W2107598941","https://openalex.org/W2145453687","https://openalex.org/W2250521169","https://openalex.org/W2251135946","https://openalex.org/W2515462165","https://openalex.org/W2517194566","https://openalex.org/W2577666659","https://openalex.org/W2604610161","https://openalex.org/W2606901057","https://openalex.org/W2739722817","https://openalex.org/W2788031953","https://openalex.org/W2887428522","https://openalex.org/W2895715183","https://openalex.org/W2962924839","https://openalex.org/W2962972936"],"related_works":["https://openalex.org/W842810586","https://openalex.org/W4319940250","https://openalex.org/W2352298027","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W4236762297","https://openalex.org/W2444550338","https://openalex.org/W2369351710","https://openalex.org/W2594363579","https://openalex.org/W2169232658"],"abstract_inverted_index":{"Sentence":[0],"relation":[1,25,61,114],"extraction":[2,26,62],"aims":[3],"to":[4,30,82],"extract":[5,83],"relational":[6],"facts":[7],"from":[8],"sentences,":[9],"which":[10],"is":[11,29,63],"an":[12],"important":[13,126,131],"task":[14,28,109],"in":[15,24,95],"natural":[16],"language":[17],"processing":[18],"(NLP)":[19],"field.":[20],"A":[21],"common":[22],"pipeline":[23],"(RE)":[27],"first":[31],"encode":[32],"a":[33,43,56,86,146],"sentence":[34,58],"into":[35],"latent":[36],"representation,":[37],"and":[38,52,128],"then":[39],"classify":[40],"it":[41],"utilizing":[42],"combination":[44],"of":[45,85,93,102,110,122,145,149,159],"attention":[46],"mechanism,":[47],"fully":[48],"connected":[49],"(FC)":[50],"layers":[51],"softmax":[53],"layer.":[54],"Therefore":[55],"good":[57],"encoder":[59],"for":[60],"essential.":[64],"Previous":[65],"models":[66],"use":[67],"shallow":[68],"convolution":[69],"neural":[70,74],"networks":[71,75],"or":[72],"recurrent":[73],"like":[76],"bi-directional":[77],"Long":[78],"Short-Term":[79],"Memory":[80],"(LSTM)":[81],"features":[84],"sentence.":[87],"To":[88,136],"enhance":[89,125,137],"the":[90,100,108,120,143,157],"representation":[91],"power":[92,144],"network,":[94],"this":[96],"paper":[97],"we":[98,140],"investigate":[99],"capability":[101],"Squeeze-and":[103],"Excitation":[104],"(SE)":[105],"module":[106,117],"on":[107],"distantly":[111],"supervised":[112],"noisy":[113],"extraction.":[115],"SE":[116],"could":[118],"learn":[119],"importance":[121],"each":[123],"filter,":[124],"filters":[127],"restrain":[129],"less":[130],"ones,":[132],"therefore":[133],"prevent":[134],"overfitting.":[135],"position":[138],"information,":[139],"also":[141],"demonstrated":[142],"new":[147],"scheme":[148],"pooling":[150],"named":[151],"double":[152],"pooling.":[153],"Experimental":[154],"results":[155],"demonstrate":[156],"effectiveness":[158],"our":[160],"method":[161],"compared":[162],"with":[163],"baseline":[164],"models.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}