{"id":"https://openalex.org/W2251703179","doi":"https://doi.org/10.3115/v1/p14-2133","title":"How much do word embeddings encode about syntax?","display_name":"How much do word embeddings encode about syntax?","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2251703179","doi":"https://doi.org/10.3115/v1/p14-2133","mag":"2251703179"},"language":"en","primary_location":{"id":"doi:10.3115/v1/p14-2133","is_oa":false,"landing_page_url":"https://doi.org/10.3115/v1/p14-2133","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"type":"article","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/A5070829652","display_name":"Jacob Andreas","orcid":"https://orcid.org/0000-0002-3141-5845"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jacob Andreas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004921249","display_name":"Dan Klein","orcid":"https://orcid.org/0000-0002-8881-1902"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Klein","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070829652"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.7705,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.99017986,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"822","last_page":"827"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/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/T12380","display_name":"Authorship Attribution and Profiling","score":0.9936000108718872,"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":0.8094028234481812},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.7915911674499512},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7227364778518677},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7195945382118225},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7030172944068909},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.6466432809829712},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6423686742782593},{"id":"https://openalex.org/keywords/syntax","display_name":"Syntax","score":0.5898462533950806},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.5490959286689758},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.42117467522621155},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3274666368961334},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.233503520488739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8094028234481812},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.7915911674499512},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7227364778518677},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7195945382118225},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7030172944068909},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.6466432809829712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6423686742782593},{"id":"https://openalex.org/C60048249","wikidata":"https://www.wikidata.org/wiki/Q37437","display_name":"Syntax","level":2,"score":0.5898462533950806},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.5490959286689758},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.42117467522621155},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3274666368961334},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.233503520488739},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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":3,"locations":[{"id":"doi:10.3115/v1/p14-2133","is_oa":false,"landing_page_url":"https://doi.org/10.3115/v1/p14-2133","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.669.1631","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.669.1631","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/P/P14/P14-2133.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.673.1994","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.1994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/%7Ejda/papers/ak_syntax_embeddings.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W77787017","https://openalex.org/W108437174","https://openalex.org/W1019104620","https://openalex.org/W2098050104","https://openalex.org/W2125573226","https://openalex.org/W2128634885","https://openalex.org/W2133280805","https://openalex.org/W2141599568","https://openalex.org/W2147152072","https://openalex.org/W2153579005","https://openalex.org/W2158139315","https://openalex.org/W2158899491","https://openalex.org/W2170206653","https://openalex.org/W2251874715","https://openalex.org/W2251987034","https://openalex.org/W2951769629"],"related_works":["https://openalex.org/W16897256","https://openalex.org/W2498524657","https://openalex.org/W4287125478","https://openalex.org/W3171594500","https://openalex.org/W2077104824","https://openalex.org/W3170826848","https://openalex.org/W2168778844","https://openalex.org/W2372020181","https://openalex.org/W2156531654","https://openalex.org/W1581723585"],"abstract_inverted_index":{"Do":[0],"continuous":[1],"word":[2,17,99],"embeddings":[3,18,77,100],"encode":[4],"any":[5],"useful":[6],"information":[7,75,103],"for":[8,45],"constituency":[9],"parsing?":[10],"We":[11,48],"isolate":[12],"three":[13],"ways":[14],"in":[15,113],"which":[16],"might":[19],"augment":[20],"a":[21,55,59,86],"state-of-the-art":[22,60],"statistical":[23],"parser:":[24],"by":[25,32,41],"connecting":[26],"out-of-vocabulary":[27],"words":[28],"to":[29,58,79,85],"known":[30],"ones,":[31],"encouraging":[33],"common":[34],"behavior":[35],"among":[36],"related":[37],"in-vocabulary":[38],"words,":[39],"and":[40],"di-rectly":[42],"providing":[43],"features":[44],"the":[46],"lexicon.":[47],"test":[49],"each":[50],"of":[51],"these":[52],"hypotheses":[53],"with":[54,88,108],"targeted":[56],"change":[57],"base-line.":[61],"Despite":[62],"small":[63,67],"gains":[64],"on":[65],"extremely":[66],"supervised":[68],"training":[69,90],"sets,":[70],"we":[71],"find":[72],"that":[73,98,104],"extra":[74],"from":[76,111],"appears":[78],"make":[80],"little":[81],"or":[82],"no":[83],"difference":[84],"parser":[87],"adequate":[89],"data.":[91],"Our":[92],"results":[93],"support":[94],"an":[95],"overall":[96],"hypothe-sis":[97],"import":[101],"syntac-tic":[102],"is":[105],"ultimately":[106],"redun-dant":[107],"distinctions":[109],"learned":[110],"tree-banks":[112],"other":[114],"ways.":[115],"1":[116]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":9},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}