{"id":"https://openalex.org/W2566563465","doi":"https://doi.org/10.18653/v1/d16-1123","title":"Convolutional Neural Network Language Models","display_name":"Convolutional Neural Network Language Models","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2566563465","doi":"https://doi.org/10.18653/v1/d16-1123","mag":"2566563465"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d16-1123","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/d16-1123","pdf_url":"https://doi.org/10.18653/v1/d16-1123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.18653/v1/d16-1123","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004365148","display_name":"Ngoc-Quan Pham","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ngoc-Quan Pham","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007916691","display_name":"Germ\u00e1n Kruszewski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Germ\u00e1n Kruszewski","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026675870","display_name":"Gemma Boleda","orcid":"https://orcid.org/0000-0001-6140-7080"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gemma Boleda","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004365148"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8597,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.97009712,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1153","last_page":"1162"},"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":0.9998999834060669,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9968000054359436,"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":0.8598839044570923},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.630790650844574},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.567527711391449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5366927981376648}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8598839044570923},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.630790650844574},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.567527711391449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5366927981376648}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/d16-1123","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/d16-1123","pdf_url":"https://doi.org/10.18653/v1/d16-1123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:repositori.upf.edu:10230/32701","is_oa":true,"landing_page_url":"http://hdl.handle.net/10230/32701","pdf_url":null,"source":{"id":"https://openalex.org/S4306402615","display_name":"Repositori digital de la UPF (Universitat Pompeu Fabra)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I170486558","host_organization_name":"Universitat Pompeu Fabra","host_organization_lineage":["https://openalex.org/I170486558"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.18653/v1/d16-1123","is_oa":true,"landing_page_url":"http://doi.org/10.18653/v1/d16-1123","pdf_url":"https://doi.org/10.18653/v1/d16-1123","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2016 Conference on Empirical Methods in Natural\n          Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2566563465.pdf","grobid_xml":"https://content.openalex.org/works/W2566563465.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W100623710","https://openalex.org/W179875071","https://openalex.org/W194986311","https://openalex.org/W1520465330","https://openalex.org/W1536929369","https://openalex.org/W1538131130","https://openalex.org/W1575384945","https://openalex.org/W1591801644","https://openalex.org/W1686810756","https://openalex.org/W1793121960","https://openalex.org/W1799366690","https://openalex.org/W1832693441","https://openalex.org/W1904365287","https://openalex.org/W1934041838","https://openalex.org/W1970689298","https://openalex.org/W2120615054","https://openalex.org/W2124807415","https://openalex.org/W2131876387","https://openalex.org/W2143719855","https://openalex.org/W2152790380","https://openalex.org/W2155870214","https://openalex.org/W2158899491","https://openalex.org/W2163605009","https://openalex.org/W2170738476","https://openalex.org/W2171928131","https://openalex.org/W2194775991","https://openalex.org/W2251622960","https://openalex.org/W2251682575","https://openalex.org/W2251994258","https://openalex.org/W2949117887","https://openalex.org/W2951559648","https://openalex.org/W3038058348"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4293226380","https://openalex.org/W4396696052","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"shown":[5],"to":[6,18,53,64],"yield":[7],"very":[8],"strong":[9],"results":[10],"in":[11,98,132,145],"several":[12],"Computer":[13,146],"Vision":[14],"tasks.":[15,100],"Their":[16],"application":[17,50],"language":[19,54,95,133],"has":[20,27],"received":[21],"much":[22],"less":[23],"attention,":[24],"and":[25,148],"it":[26],"mainly":[28],"focused":[29],"on":[30],"static":[31],"classification":[32,37],"tasks,":[33],"such":[34],"as":[35,67,69,135,157,159],"sentence":[36],"for":[38,94,102],"Sentiment":[39],"Analysis":[40],"or":[41],"relation":[42],"extraction.":[43],"In":[44],"this":[45],"work,":[46],"we":[47,78,119],"study":[48],"the":[49,114,124,127,150,163],"of":[51,113,123,126,142],"CNNs":[52,81,131],"modeling,":[55],"a":[56,139],"dynamic,":[57],"sequential":[58,99],"prediction":[59],"task":[60],"that":[61,80,130,149],"needs":[62],"models":[63],"capture":[65],"local":[66],"well":[68],"long-range":[70],"dependency":[71],"information.":[72],"Our":[73],"contribution":[74],"is":[75,110],"twofold.":[76],"First,":[77],"show":[79],"achieve":[82],"11-26%":[83],"better":[84],"absolute":[85],"performance":[86],"than":[87],"feed-forward":[88],"neural
\\nlanguage":[89],"models,":[90,104],"demonstrating":[91],"their":[92],"potential":[93],"representation":[96],"even":[97],"As":[101],"recurrent":[103],"our":[105],"model":[106,151],"outperforms":[107],"RNNs":[108],"but":[109],"below":[111],"state":[112],"art":[115],"LSTM":[116],"models.":[117],"Second,":[118],"gain":[120],"some":[121],"understanding":[122],"behavior":[125],"model,":[128],"showing":[129],"act":[134],"feature":[136],"detectors":[137],"at":[138],"high":[140],"level":[141],"abstraction,":[143],"like":[144],"Vision,":[147],"can":[152],"profitably":[153],"use":[154],"information":[155],"from":[156],"far":[158],"16":[160],"words":[161],"before":[162],"target.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-01T08:55:55.761014","created_date":"2025-10-10T00:00:00"}