{"id":"https://openalex.org/W4294094045","doi":"https://doi.org/10.48550/arxiv.1612.04118","title":"Information Extraction with Character-level Neural Networks and Free Noisy Supervision","display_name":"Information Extraction with Character-level Neural Networks and Free Noisy Supervision","publication_year":2016,"publication_date":"2016-12-13","ids":{"openalex":"https://openalex.org/W4294094045","doi":"https://doi.org/10.48550/arxiv.1612.04118"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1612.04118","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1612.04118","pdf_url":"https://arxiv.org/pdf/1612.04118","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},"parent_works":[],"lineage":["https://openalex.org/W4294094045"],"child_works_count":0,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1612.04118","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032660352","display_name":"Philipp Meerkamp","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1299907687","display_name":"Bloomberg (United States)","ror":"https://ror.org/02rdpzb15","country_code":"US","type":"company","lineage":["https://openalex.org/I1299907687"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meerkamp, Philipp","raw_affiliation_strings":["Bloomberg LP"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bloomberg LP","institution_ids":["https://openalex.org/I1299907687"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045788834","display_name":"Zhengyi Zhou","orcid":"https://orcid.org/0000-0002-0285-4638","observed_orcids":["https://orcid.org/0000-0002-0285-4638"]},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhou, Zhengyi","raw_affiliation_strings":["AT&T Labs Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"AT&T Labs Research","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.7157999873161316,"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":0.7157999873161316,"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":0.20679999887943268,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/information-extraction","display_name":"information extraction","score":0.9929999709129333},{"id":"https://openalex.org/keywords/noisy-supervision","display_name":"noisy supervision","score":0.9779999852180481},{"id":"https://openalex.org/keywords/database-consistency","display_name":"database consistency","score":0.5699999928474426},{"id":"https://openalex.org/keywords/financial-language","display_name":"financial language","score":0.5669999718666077},{"id":"https://openalex.org/keywords/precision-improvement","display_name":"precision improvement","score":0.37299999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8115618228912354},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7800923585891724},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7161860466003418},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.634199321269989},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.602975606918335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5562105178833008},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5542572140693665},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5493316054344177},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5220717787742615},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.45955100655555725},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.4495689272880554},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44328927993774414},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3523526191711426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34874236583709717},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1612.04118","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1612.04118","pdf_url":"https://arxiv.org/pdf/1612.04118","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.1612.04118","doi":"https://doi.org/10.48550/arxiv.1612.04118","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1612.04118","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1612.04118","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1612.04118","pdf_url":"https://arxiv.org/pdf/1612.04118","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4294094045.pdf","grobid_xml":"https://content.openalex.org/works/W4294094045.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W1591801644","https://openalex.org/W2179519966"],"related_works":["https://openalex.org/W2561174357","https://openalex.org/W2757590513","https://openalex.org/W6941252417","https://openalex.org/W2904527529","https://openalex.org/W3106012260","https://openalex.org/W2363801401","https://openalex.org/W7100608211","https://openalex.org/W2738002547","https://openalex.org/W1668750667","https://openalex.org/W164869738"],"abstract_inverted_index":{"We":[0],"present":[1],"an":[2,11],"architecture":[3,41],"for":[4,99],"information":[5,47,93],"extraction":[6,48,94],"from":[7],"text":[8],"that":[9],"augments":[10],"existing":[12,32,76],"parser":[13],"with":[14,31,57],"a":[15,24,35,86],"character-level":[16],"neural":[17,62],"network.":[18],"The":[19],"network":[20],"is":[21],"trained":[22],"using":[23],"measure":[25],"of":[26,28,37,45,60,68],"consistency":[27],"extracted":[29],"data":[30,69],"databases":[33],"as":[34],"form":[36],"noisy":[38],"supervision.":[39],"Our":[40],"combines":[42],"the":[43,58,75,79],"ability":[44,59],"constraint-based":[46,91],"systems":[49],"to":[50,64,70,82],"easily":[51],"incorporate":[52],"domain":[53],"knowledge":[54],"and":[55,88],"constraints":[56],"deep":[61],"networks":[63],"leverage":[65],"large":[66,83],"amounts":[67],"learn":[71],"complex":[72],"features.":[73],"Boosting":[74],"parser's":[77],"precision,":[78],"system":[80,95],"led":[81],"improvements":[84],"over":[85],"mature":[87],"highly":[89],"tuned":[90],"production":[92],"used":[96],"at":[97],"Bloomberg":[98],"financial":[100],"language":[101],"text.":[102]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-09-01T00:00:00"}
