{"id":"https://openalex.org/W2250968750","doi":"https://doi.org/10.18653/v1/d13-1117","title":"Feature Noising for Log-Linear Structured Prediction","display_name":"Feature Noising for Log-Linear Structured Prediction","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2250968750","doi":"https://doi.org/10.18653/v1/d13-1117","mag":"2250968750"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d13-1117","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/d13-1117","pdf_url":"https://aclanthology.org/D13-1117.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/D13-1117.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102727943","display_name":"Sida Wang","orcid":"https://orcid.org/0000-0001-8101-8883"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sida Wang","raw_affiliation_strings":["Stanford University, Stanford, United States"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, United States","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013876132","display_name":"Mengqiu Wang","orcid":"https://orcid.org/0000-0002-0465-1324"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengqiu Wang","raw_affiliation_strings":["Stanford University, Stanford, United States"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, United States","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089493027","display_name":"Stefan Wager","orcid":"https://orcid.org/0000-0002-7526-9077"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stefan Wager","raw_affiliation_strings":["Stanford University, Stanford, United States"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, United States","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025255782","display_name":"Percy Liang","orcid":"https://orcid.org/0000-0002-0458-6139"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Percy Liang","raw_affiliation_strings":["Stanford University, Stanford, United States"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, United States","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046006076","display_name":"Christopher D. Manning","orcid":"https://orcid.org/0000-0001-6155-649X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher D. Manning","raw_affiliation_strings":["Stanford University, Stanford, United States"],"affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, United States","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102727943"],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":7.9078,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.97238654,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1170","last_page":"1179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9969000220298767,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9969000220298767,"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":0.9968000054359436,"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"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9929999709129333,"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/overfitting","display_name":"Overfitting","score":0.8305472731590271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.699908435344696},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6659534573554993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6153993606567383},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.4954341650009155},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4649782180786133},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46246784925460815},{"id":"https://openalex.org/keywords/crfs","display_name":"CRFS","score":0.45839202404022217},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4489806592464447},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.2613147497177124},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09578433632850647}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8305472731590271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699908435344696},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6659534573554993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6153993606567383},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.4954341650009155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4649782180786133},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46246784925460815},{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.45839202404022217},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4489806592464447},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.2613147497177124},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09578433632850647},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.18653/v1/d13-1117","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/d13-1117","pdf_url":"https://aclanthology.org/D13-1117.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.409.2534","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.2534","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cs.stanford.edu/people/mengqiu/publication/emnlp13a.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.475.5299","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.475.5299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-nlp.stanford.edu/pubs/sidaw13feature.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.593.1798","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.593.1798","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/D/D13/D13-1117.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/d13-1117","is_oa":true,"landing_page_url":"http://dx.doi.org/10.18653/v1/d13-1117","pdf_url":"https://aclanthology.org/D13-1117.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 2013 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2250968750.pdf","grobid_xml":"https://content.openalex.org/works/W2250968750.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W35527955","https://openalex.org/W141372029","https://openalex.org/W1904365287","https://openalex.org/W1995945562","https://openalex.org/W2015140204","https://openalex.org/W2057853719","https://openalex.org/W2096765155","https://openalex.org/W2100341991","https://openalex.org/W2104867159","https://openalex.org/W2105103433","https://openalex.org/W2107008379","https://openalex.org/W2109094355","https://openalex.org/W2111406701","https://openalex.org/W2141416357","https://openalex.org/W2144578941","https://openalex.org/W2150969560","https://openalex.org/W2152722485","https://openalex.org/W2158542502","https://openalex.org/W2160059488","https://openalex.org/W2160469620","https://openalex.org/W2166093887","https://openalex.org/W2167216307","https://openalex.org/W2293363371"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W2055466819","https://openalex.org/W1574414179","https://openalex.org/W3022161193","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W4295602020","https://openalex.org/W2800507189","https://openalex.org/W2922073769","https://openalex.org/W4281702477"],"abstract_inverted_index":{"NLP":[0],"models":[1],"have":[2],"many":[3],"and":[4,7,42,73,115],"sparse":[5],"features,":[6],"regularization":[8,21],"is":[9,22,94],"key":[10,78],"for":[11],"balancing":[12],"model":[13],"overfitting":[14],"versus":[15],"underfitting.A":[16],"recently":[17],"repopularized":[18],"form":[19],"of":[20,80,89,127],"to":[23,32,62,66,85,112],"generate":[24],"fake":[25,58],"training":[26,54],"data":[27],"by":[28],"repeatedly":[29],"adding":[30],"noise":[31],"real":[33],"data.We":[34,59],"reinterpret":[35],"this":[36,64],"noising":[37],"as":[38],"an":[39],"explicit":[40],"regularizer,":[41],"approximate":[43],"it":[44,103],"with":[45],"a":[46,82,95,107,120],"second-order":[47],"formula":[48],"that":[49],"can":[50,101],"be":[51],"used":[52],"during":[53],"without":[55],"actually":[56],"generating":[57],"show":[60],"how":[61],"apply":[63],"method":[65,118],"structured":[67],"prediction":[68],"using":[69],"multinomial":[70],"logistic":[71],"regression":[72],"linear-chain":[74],"CRFs.We":[75],"tackle":[76],"the":[77,87,90],"challenge":[79],"developing":[81],"dynamic":[83],"program":[84],"compute":[86],"gradient":[88],"regularizer":[91,93],"efficiently.The":[92],"sum":[96],"over":[97,125],"inputs,":[98],"so":[99],"we":[100],"estimate":[102],"more":[104],"accurately":[105],"via":[106],"semi-supervised":[108],"or":[109],"transductive":[110],"extension.Applied":[111],"text":[113],"classification":[114],"NER,":[116],"our":[117],"provides":[119],">1%":[121],"absolute":[122],"performance":[123],"gain":[124],"use":[126],"standard":[128],"L":[129],"2":[130],"regularization.":[131]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
