{"id":"https://openalex.org/W2736266458","doi":"https://doi.org/10.1109/icnidc.2016.7974612","title":"Learning mention and relation representation with convolutional neural networks for relation extraction","display_name":"Learning mention and relation representation with convolutional neural networks for relation extraction","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2736266458","doi":"https://doi.org/10.1109/icnidc.2016.7974612","mag":"2736266458"},"language":"en","primary_location":{"id":"doi:10.1109/icnidc.2016.7974612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5085036402","display_name":"Shuohong Liang","orcid":null},"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":"Shuohong Liang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","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":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100391896","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-1717-5785"},"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":"Wei Wang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.1941,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55890252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"437","last_page":"441"},"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":0.996999979019165,"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/relationship-extraction","display_name":"Relationship extraction","score":0.8264673948287964},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.8152066469192505},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7561968564987183},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6297032237052917},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6220596432685852},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6113402843475342},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5994665622711182},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5703878998756409},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5635297298431396},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5427243709564209},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5384699106216431},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5279870629310608},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5116317868232727},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.4685674011707306},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.45088374614715576},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.43530553579330444},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.25340062379837036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1542859971523285}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8264673948287964},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.8152066469192505},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561968564987183},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6297032237052917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6220596432685852},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6113402843475342},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5994665622711182},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5703878998756409},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5635297298431396},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5427243709564209},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5384699106216431},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5279870629310608},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5116317868232727},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.4685674011707306},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.45088374614715576},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.43530553579330444},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25340062379837036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1542859971523285},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnidc.2016.7974612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnidc.2016.7974612","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.46000000834465027,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W174427690","https://openalex.org/W1551842868","https://openalex.org/W1604644367","https://openalex.org/W1832693441","https://openalex.org/W2096765155","https://openalex.org/W2107598941","https://openalex.org/W2132679783","https://openalex.org/W2148007012","https://openalex.org/W2153579005","https://openalex.org/W2155454737","https://openalex.org/W2158899491","https://openalex.org/W2951723246","https://openalex.org/W2952230511","https://openalex.org/W4294170691","https://openalex.org/W6600284362","https://openalex.org/W6607091552","https://openalex.org/W6638311095","https://openalex.org/W6679781796","https://openalex.org/W6681674953","https://openalex.org/W6682691769","https://openalex.org/W6683738474"],"related_works":["https://openalex.org/W2487162673","https://openalex.org/W2793211469","https://openalex.org/W2949152769","https://openalex.org/W4372354731","https://openalex.org/W2942366970","https://openalex.org/W2807634898","https://openalex.org/W1692008701","https://openalex.org/W2597588799","https://openalex.org/W1968265719","https://openalex.org/W4286432911"],"abstract_inverted_index":{"Most":[0],"previous":[1],"works":[2],"for":[3,85],"relation":[4,55,93],"extraction":[5],"are":[6],"based":[7],"on":[8],"handcrafting":[9],"sentence-level":[10],"features":[11,84],"such":[12],"as":[13,82],"part":[14],"of":[15,34,66,70],"speech,":[16],"named":[17],"entity":[18],"and":[19,37,54,72,92,95],"dependency":[20],"tree":[21],"path":[22],"properties.":[23],"This":[24],"paper":[25],"proposes":[26],"a":[27,45,60,67],"new":[28],"approach":[29,78],"to":[30,62,98],"learn":[31,89],"the":[32,35,51,64],"embedding":[33],"mentions":[36],"their":[38],"relations":[39],"using":[40,79],"convolutional":[41],"neural":[42],"networks":[43],"with":[44],"pairwise":[46],"ranking":[47],"loss":[48],"function.":[49],"Through":[50],"learned":[52],"mention":[53,71,91],"embeddings":[56,81],"we":[57],"can":[58,88],"get":[59],"score":[61],"evaluate":[63],"relevance":[65],"given":[68],"pair":[69],"relation.":[73],"We":[74],"show":[75],"that":[76],"our":[77,86],"word":[80],"input":[83],"model":[87],"better":[90],"representation":[94],"is":[96],"superior":[97],"state-of-the-art":[99],"results.":[100]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}