{"id":"https://openalex.org/W2898810549","doi":"https://doi.org/10.18653/v1/k18-1020","title":"Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?","display_name":"Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2898810549","doi":"https://doi.org/10.18653/v1/k18-1020","mag":"2898810549"},"language":"en","primary_location":{"id":"doi:10.18653/v1/k18-1020","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k18-1020","pdf_url":"https://www.aclweb.org/anthology/K18-1020.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/K18-1020.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065358874","display_name":"Eylon Shoshan","orcid":null},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Eylon Shoshan","raw_affiliation_strings":["Department of Computer Science, Technion -Israel Institute of Technology, Haifa, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Technion -Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029708595","display_name":"Kira Radinsky","orcid":"https://orcid.org/0009-0007-7918-2204"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":true,"raw_author_name":"Kira Radinsky","raw_affiliation_strings":["Department of Computer Science, Technion -Israel Institute of Technology, Haifa, Israel","eBay Research, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Technion -Israel Institute of Technology, Haifa, Israel","institution_ids":["https://openalex.org/I174306211"]},{"raw_affiliation_string":"eBay Research, Israel","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5029708595"],"corresponding_institution_ids":["https://openalex.org/I174306211"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.12759659,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"200","last_page":"210"},"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":0.9995999932289124,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8522394895553589},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7117970585823059},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6859642267227173},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6589571237564087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6573150157928467},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.636441171169281},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"Named-entity recognition","score":0.6326379776000977},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5641373991966248},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5587575435638428},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5526257753372192},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5106630921363831},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.45011672377586365},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33198654651641846},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3253616690635681}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8522394895553589},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7117970585823059},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6859642267227173},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6589571237564087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6573150157928467},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.636441171169281},{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.6326379776000977},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5641373991966248},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5587575435638428},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5526257753372192},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5106630921363831},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.45011672377586365},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33198654651641846},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3253616690635681},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/k18-1020","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k18-1020","pdf_url":"https://www.aclweb.org/anthology/K18-1020.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Conference on Computational Natural Language Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/k18-1020","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/k18-1020","pdf_url":"https://www.aclweb.org/anthology/K18-1020.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd Conference on Computational Natural Language Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.75,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2898810549.pdf","grobid_xml":"https://content.openalex.org/works/W2898810549.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1939882552","https://openalex.org/W2004763266","https://openalex.org/W2028133290","https://openalex.org/W2064675550","https://openalex.org/W2070808142","https://openalex.org/W2088911157","https://openalex.org/W2095705004","https://openalex.org/W2103076621","https://openalex.org/W2131774270","https://openalex.org/W2143104527","https://openalex.org/W2143345705","https://openalex.org/W2144578941","https://openalex.org/W2157331557","https://openalex.org/W2158760659","https://openalex.org/W2162357792","https://openalex.org/W2212703438","https://openalex.org/W2236206745","https://openalex.org/W2252016937","https://openalex.org/W2282821441","https://openalex.org/W2462891382","https://openalex.org/W2515248967","https://openalex.org/W2516809705","https://openalex.org/W2741887298","https://openalex.org/W2752172973","https://openalex.org/W2757311323","https://openalex.org/W2758310181","https://openalex.org/W2786432334","https://openalex.org/W2914746235","https://openalex.org/W2952087486","https://openalex.org/W2963266340","https://openalex.org/W2963625095","https://openalex.org/W2963779652","https://openalex.org/W2964121744","https://openalex.org/W2964266863","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W4250494529","https://openalex.org/W1964783010","https://openalex.org/W2399696375","https://openalex.org/W45206245","https://openalex.org/W2211396092","https://openalex.org/W2061834489","https://openalex.org/W2078793151","https://openalex.org/W2751906762","https://openalex.org/W3088215229","https://openalex.org/W3047727388"],"abstract_inverted_index":{"Named-entity":[0],"Recognition":[1],"(NER)":[2],"is":[3,13,62,71,189],"an":[4,72],"important":[5],"task":[6,137,191],"in":[7,21,32,75,100,143,177],"the":[8,27,33,41,49,76,83,157,174,178],"NLP":[9],"field":[10],",":[11],"and":[12,134,168,198,201,205],"widely":[14],"used":[15],"to":[16],"solve":[17],"many":[18,22,196],"challenges.":[19],"However,":[20],"scenarios,":[23],"not":[24,63,108],"all":[25,172],"of":[26,85,120,165,173],"entities":[28,97,122,176],"are":[29,98,107,149,180],"explicitly":[30,109],"mentioned":[31,64,184],"text.":[34],"Sometimes":[35],"they":[36,106],"could":[37],"be":[38],"inferred":[39],"from":[40,44,156],"context":[42],"or":[43],"other":[45],"indicative":[46],"words.":[47],"Consider":[48],"following":[50],"sentence:":[51],"\"CMA":[52],"can":[53,67],"easily":[54],"hydrolyze":[55],"into":[56],"free":[57],"acetic":[58],"acid.\"":[59],"Although":[60],"water":[61],"explicitly,":[65],"one":[66],"infer":[68],"that":[69,117,126,192],"H2O":[70],"entity":[73],"involved":[74,175],"process.":[77],"In":[78],"this":[79],"work,":[80],"we":[81,112],"present":[82,91],"problem":[84],"Latent":[86],"Entities":[87],"Extraction":[88],"(LEE).":[89],"We":[90,124,186],"several":[92],"methods":[93],"for":[94,169],"determining":[95],"whether":[96],"discussed":[99],"a":[101,114,135,152,190],"text,":[102],"even":[103],"though,":[104],"potentially,":[105],"written.":[110],"Specifically,":[111],"design":[113],"neural":[115],"model":[116],"handles":[118],"extraction":[119],"multiple":[121],"jointly.":[123],"show":[125],"our":[127],"model,":[128],"along":[129],"with":[130],"multi-task":[131],"learning":[132],"approach":[133],"novel":[136],"grouping":[138],"algorithm,":[139],"reaches":[140],"high":[141],"performance":[142],"identifying":[144],"latent":[145],"entities.":[146],"Our":[147],"experiments":[148],"conducted":[150],"on":[151],"large":[153],"biological":[154,166],"dataset":[155,161],"biochemical":[158],"field.":[159],"The":[160],"contains":[162],"text":[163,203],"descriptions":[164],"processes,":[167],"each":[170],"process,":[171],"process":[179],"labeled,":[181],"including":[182],"implicitly":[183],"ones.":[185],"believe":[187],"LEE":[188],"will":[193],"significantly":[194],"improve":[195,202],"NER":[197],"subsequent":[199],"applications":[200],"understanding":[204],"inference.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
