{"id":"https://openalex.org/W2251847161","doi":"https://doi.org/10.3115/v1/d14-1164","title":"Combining Distant and Partial Supervision for Relation Extraction","display_name":"Combining Distant and Partial Supervision for Relation Extraction","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2251847161","doi":"https://doi.org/10.3115/v1/d14-1164","mag":"2251847161"},"language":"en","primary_location":{"id":"doi:10.3115/v1/d14-1164","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1164","pdf_url":"https://aclanthology.org/D14-1164.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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/D14-1164.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074606190","display_name":"Gabor Angeli","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gabor Angeli","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079191082","display_name":"Julie Tibshirani","orcid":"https://orcid.org/0009-0009-1388-6406"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Julie Tibshirani","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047624579","display_name":"Jean Y. Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jean Wu","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046006076","display_name":"Christopher D. Manning","orcid":"https://orcid.org/0000-0001-6155-649X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher D. Manning","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":150,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1556","last_page":"1567"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9997000098228455,"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/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9977999925613403,"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/relation","display_name":"Relation (database)","score":0.692667543888092},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5724247097969055},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5614324808120728},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.539461612701416},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.19636663794517517},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.07420629262924194},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06316116452217102}],"concepts":[{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.692667543888092},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5724247097969055},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5614324808120728},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.539461612701416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.19636663794517517},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.07420629262924194},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06316116452217102}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3115/v1/d14-1164","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1164","pdf_url":"https://aclanthology.org/D14-1164.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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.455.4161","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.4161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://stanford.edu/~angeli/papers/2014-emnlp-activelearning.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.672.3691","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.3691","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://emnlp2014.org/papers/pdf/EMNLP2014164.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.696.2557","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.2557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://aclweb.org/anthology/D/D14/D14-1164.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/v1/d14-1164","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/d14-1164","pdf_url":"https://aclanthology.org/D14-1164.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 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251847161.pdf","grobid_xml":"https://content.openalex.org/works/W2251847161.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W32770071","https://openalex.org/W174427690","https://openalex.org/W1514707997","https://openalex.org/W1528361845","https://openalex.org/W1547214099","https://openalex.org/W1604644367","https://openalex.org/W1852412531","https://openalex.org/W1954715867","https://openalex.org/W2020954089","https://openalex.org/W2053238041","https://openalex.org/W2065010255","https://openalex.org/W2085989833","https://openalex.org/W2100249433","https://openalex.org/W2102267996","https://openalex.org/W2107598941","https://openalex.org/W2110327402","https://openalex.org/W2128678390","https://openalex.org/W2132679783","https://openalex.org/W2146304342","https://openalex.org/W2250265269","https://openalex.org/W2251014694","https://openalex.org/W2251832915","https://openalex.org/W2251960799","https://openalex.org/W2295082727","https://openalex.org/W2402842738","https://openalex.org/W2407338347","https://openalex.org/W2408657266","https://openalex.org/W2903158431","https://openalex.org/W2915395058","https://openalex.org/W3112138688"],"related_works":["https://openalex.org/W2976808399","https://openalex.org/W2609844752","https://openalex.org/W2981341912","https://openalex.org/W4385734297","https://openalex.org/W4285246823","https://openalex.org/W4226278302","https://openalex.org/W2547211086","https://openalex.org/W4221160509","https://openalex.org/W3114142812","https://openalex.org/W4380551175"],"abstract_inverted_index":{"Broad-coverage":[0],"relation":[1,29],"extraction":[2],"either":[3],"requires":[4],"expensive":[5],"supervised":[6,28,80],"training":[7],"data,":[8],"or":[9],"suffers":[10],"from":[11],"drawbacks":[12],"inherent":[13],"to":[14,25,51],"distant":[15],"supervision.":[16],"We":[17,39],"present":[18],"an":[19],"approach":[20,83],"for":[21,70],"providing":[22],"partial":[23],"supervision":[24,69],"a":[26,32,48,77,85,101],"distantly":[27,79],"extractor":[30],"using":[31],"small":[33],"number":[34],"of":[35,67,76,88,105],"carefully":[36],"selected":[37],"examples.":[38],"compare":[40],"against":[41],"established":[42],"active":[43],"learning":[44],"criteria":[45],"and":[46,58],"propose":[47],"novel":[49],"criterion":[50],"sample":[52],"examples":[53,72],"which":[54],"are":[55],"both":[56],"uncertain":[57],"representative.":[59],"In":[60],"this":[61],"way,":[62],"we":[63],"combine":[64],"the":[65,74,94],"benefits":[66],"fine-grained":[68],"difficult":[71],"with":[73],"coverage":[75],"large":[78],"corpus.":[81],"Our":[82],"gives":[84],"substantial":[86],"increase":[87],"3.9%":[89],"endto-end":[90],"F":[91,103],"1":[92,104],"on":[93],"2013":[95],"KBP":[96],"Slot":[97],"Filling":[98],"evaluation,":[99],"yielding":[100],"net":[102],"37.7%.":[106]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":26},{"year":2017,"cited_by_count":16},{"year":2016,"cited_by_count":21},{"year":2015,"cited_by_count":17},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
