{"id":"https://openalex.org/W2964167098","doi":"https://doi.org/10.18653/v1/p16-1105","title":"End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures","display_name":"End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2964167098","doi":"https://doi.org/10.18653/v1/p16-1105","mag":"2964167098"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p16-1105","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1105","pdf_url":"https://www.aclweb.org/anthology/P16-1105.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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P16-1105.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101412540","display_name":"Makoto Miwa","orcid":"https://orcid.org/0000-0002-2330-6972"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Makoto Miwa","raw_affiliation_strings":["Toyota Technological Institute Nagoya, 468-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute Nagoya, 468-8511, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001987532","display_name":"Mohit Bansal","orcid":"https://orcid.org/0000-0001-5522-1351"},"institutions":[{"id":"https://openalex.org/I160992636","display_name":"Toyota Technological Institute at Chicago","ror":"https://ror.org/02sn5gb64","country_code":"US","type":"education","lineage":["https://openalex.org/I160992636"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohit Bansal","raw_affiliation_strings":["Toyota Technological Institute at Chicago Chicago, IL, 60637, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Technological Institute at Chicago Chicago, IL, 60637, USA","institution_ids":["https://openalex.org/I160992636"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101412540"],"corresponding_institution_ids":["https://openalex.org/I4840577"],"apc_list":null,"apc_paid":null,"fwci":139.5873,"has_fulltext":true,"cited_by_count":1258,"citation_normalized_percentile":{"value":0.99964108,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1105","last_page":"1116"},"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":0.9998999834060669,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9921000003814697,"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":0.7338929176330566},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7165220379829407},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.632949948310852},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.5915453433990479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44573405385017395},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4379042387008667},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34375685453414917},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32048287987709045},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2146751880645752},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15546607971191406},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07383975386619568}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7338929176330566},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7165220379829407},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.632949948310852},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.5915453433990479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44573405385017395},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4379042387008667},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34375685453414917},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32048287987709045},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2146751880645752},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15546607971191406},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07383975386619568},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p16-1105","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1105","pdf_url":"https://www.aclweb.org/anthology/P16-1105.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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p16-1105","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p16-1105","pdf_url":"https://www.aclweb.org/anthology/P16-1105.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 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964167098.pdf","grobid_xml":"https://content.openalex.org/works/W2964167098.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W648786980","https://openalex.org/W1522301498","https://openalex.org/W1551842868","https://openalex.org/W1567199133","https://openalex.org/W1750263989","https://openalex.org/W1816079941","https://openalex.org/W1866795174","https://openalex.org/W1889268436","https://openalex.org/W1914293925","https://openalex.org/W1924762813","https://openalex.org/W1940872118","https://openalex.org/W2004763266","https://openalex.org/W2020278455","https://openalex.org/W2042188227","https://openalex.org/W2051390224","https://openalex.org/W2053238041","https://openalex.org/W2077054525","https://openalex.org/W2079735306","https://openalex.org/W2095705004","https://openalex.org/W2112628236","https://openalex.org/W2115834228","https://openalex.org/W2132516856","https://openalex.org/W2133439966","https://openalex.org/W2134033474","https://openalex.org/W2138627627","https://openalex.org/W2143612262","https://openalex.org/W2150355110","https://openalex.org/W2153579005","https://openalex.org/W2155454737","https://openalex.org/W2162590473","https://openalex.org/W2181042685","https://openalex.org/W2250710764","https://openalex.org/W2250861254","https://openalex.org/W2251091211","https://openalex.org/W2407338347","https://openalex.org/W2465041517","https://openalex.org/W2953391617","https://openalex.org/W2963148156","https://openalex.org/W2963355447","https://openalex.org/W2964121744","https://openalex.org/W2964217331","https://openalex.org/W2989631226","https://openalex.org/W4285719527","https://openalex.org/W4294170691"],"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":{"We":[0,56,104],"present":[1,131],"a":[2,53],"novel":[3],"end-to-end":[4],"neural":[5,16],"model":[6,19,41,79,85,111,119,138],"to":[7,42,114],"extract":[8],"entities":[9,46,61],"and":[10,24,47,64,75,92,101],"relations":[11,48],"between":[12],"them.":[13],"Our":[14,78],"recurrent":[15],"network":[17],"based":[18,110,118],"captures":[20],"both":[21,45],"word":[22],"sequence":[23],"dependency":[25],"tree":[26],"substructure":[27],"information":[28,68],"by":[29],"stacking":[30],"bidirectional":[31,35],"treestructured":[32],"LSTM-RNNs":[33],"on":[34,86,99,122],"sequential":[36],"LSTM-RNNs.":[37],"This":[38],"allows":[39],"our":[40,108],"jointly":[43],"represent":[44],"with":[49],"shared":[50],"parameters":[51],"in":[52,69,97],"single":[54],"model.":[55],"further":[57],"encourage":[58],"detection":[59],"of":[60,66,136],"during":[62],"training":[63],"use":[65],"entity":[67,73],"relation":[70,88,124],"extraction":[71],"via":[72],"pretraining":[74],"scheduled":[76],"sampling.":[77],"improves":[80],"over":[81],"the":[82,115],"stateof-the-art":[83],"feature-based":[84],"end-toend":[87],"extraction,":[89],"achieving":[90],"12.1%":[91],"5.7%":[93],"relative":[94],"error":[95],"reductions":[96],"F1score":[98],"ACE2005":[100],"ACE2004,":[102],"respectively.":[103],"also":[105],"show":[106],"that":[107],"LSTM-RNN":[109],"compares":[112],"favorably":[113],"state-of-the-art":[116],"CNN":[117],"(in":[120],"F1-score)":[121],"nominal":[123],"classification":[125],"(SemEval-2010":[126],"Task":[127],"8).":[128],"Finally,":[129],"we":[130],"an":[132],"extensive":[133],"ablation":[134],"analysis":[135],"several":[137],"components.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":73},{"year":2024,"cited_by_count":115},{"year":2023,"cited_by_count":165},{"year":2022,"cited_by_count":161},{"year":2021,"cited_by_count":226},{"year":2020,"cited_by_count":194},{"year":2019,"cited_by_count":169},{"year":2018,"cited_by_count":98},{"year":2017,"cited_by_count":43},{"year":2016,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}