{"id":"https://openalex.org/W2252194689","doi":"https://doi.org/10.3115/v1/e14-1018","title":"Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing","display_name":"Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2252194689","doi":"https://doi.org/10.3115/v1/e14-1018","mag":"2252194689"},"language":"en","primary_location":{"id":"doi:10.3115/v1/e14-1018","is_oa":true,"landing_page_url":"http://doi.org/10.3115/v1/e14-1018","pdf_url":"https://doi.org/10.3115/v1/e14-1018","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3115/v1/e14-1018","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046515047","display_name":"Kaixu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kaixu Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066326238","display_name":"Jinsong Su","orcid":"https://orcid.org/0000-0001-5606-7122"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinsong Su","raw_affiliation_strings":["[Xiamen University]"],"affiliations":[{"raw_affiliation_string":"[Xiamen University]","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101399826","display_name":"Changle Zhou","orcid":"https://orcid.org/0000-0002-6779-7670"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changle Zhou","raw_affiliation_strings":["[Xiamen University]"],"affiliations":[{"raw_affiliation_string":"[Xiamen University]","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046515047"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.818,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.82138652,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"164","last_page":"173"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10028","display_name":"Topic Modeling","score":0.9988999962806702,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/dependency-grammar","display_name":"Dependency grammar","score":0.8294558525085449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8174918293952942},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7378348112106323},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.7295817136764526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7096758484840393},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6193759441375732},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.61622154712677},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.534809947013855},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.5011906623840332},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.45077750086784363},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44636839628219604},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43627652525901794},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.391136109828949},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3863464593887329}],"concepts":[{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.8294558525085449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8174918293952942},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7378348112106323},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7295817136764526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7096758484840393},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6193759441375732},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.61622154712677},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.534809947013855},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.5011906623840332},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.45077750086784363},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44636839628219604},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43627652525901794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.391136109828949},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3863464593887329}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3115/v1/e14-1018","is_oa":true,"landing_page_url":"http://doi.org/10.3115/v1/e14-1018","pdf_url":"https://doi.org/10.3115/v1/e14-1018","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.3115/v1/e14-1018","is_oa":true,"landing_page_url":"http://doi.org/10.3115/v1/e14-1018","pdf_url":"https://doi.org/10.3115/v1/e14-1018","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2252194689.pdf","grobid_xml":"https://content.openalex.org/works/W2252194689.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W25062297","https://openalex.org/W86604388","https://openalex.org/W162171320","https://openalex.org/W1592796124","https://openalex.org/W1598566484","https://openalex.org/W1904365287","https://openalex.org/W2008652694","https://openalex.org/W2025768430","https://openalex.org/W2096199223","https://openalex.org/W2098919260","https://openalex.org/W2104820387","https://openalex.org/W2104917081","https://openalex.org/W2106129230","https://openalex.org/W2110974939","https://openalex.org/W2116983617","https://openalex.org/W2120661206","https://openalex.org/W2124861326","https://openalex.org/W2131148434","https://openalex.org/W2138302120","https://openalex.org/W2140997362","https://openalex.org/W2142898321","https://openalex.org/W2144513243","https://openalex.org/W2145905222","https://openalex.org/W2159406587","https://openalex.org/W2160513510","https://openalex.org/W2250505282","https://openalex.org/W3112842904"],"related_works":["https://openalex.org/W2251084681","https://openalex.org/W287510790","https://openalex.org/W2098784136","https://openalex.org/W2968543375","https://openalex.org/W2571817549","https://openalex.org/W1541975828","https://openalex.org/W2888625260","https://openalex.org/W3035970863","https://openalex.org/W4288558800","https://openalex.org/W2250525544"],"abstract_inverted_index":{"Structured":[0],"perceptron":[1,122],"becomes":[2],"popular":[3],"for":[4,123],"various":[5],"NLP":[6,16,96],"tasks":[7,127],"such":[8],"as":[9],"tagging":[10,103],"and":[11,72,104],"parsing.":[12,106],"Practical":[13],"studies":[14],"on":[15,94],"did":[17],"not":[18],"pay":[19],"much":[20],"attention":[21],"to":[22,40,47,63,67,75,119,131],"its":[23],"regularization.":[24],"In":[25],"this":[26],"paper,":[27],"we":[28],"study":[29],"three":[30,95],"simple":[31],"but":[32],"effective":[33],"task-independent":[34],"regularization":[35,109],"methods:":[36],"(1)":[37],"one":[38,61,73],"is":[39,62,74,84],"average":[41],"weights":[42],"of":[43,56,125],"different":[44],"trained":[45],"models":[46],"reduce":[48],"the":[49,53,57,68,78,88,114,120],"bias":[50],"caused":[51],"by":[52],"specific":[54],"order":[55],"training":[58,82],"examples;":[59],"(2)":[60],"add":[64],"penalty":[65],"term":[66],"loss":[69],"function;":[70],"(3)":[71],"randomly":[76],"corrupt":[77],"data":[79],"flow":[80],"during":[81],"which":[83],"called":[85],"dropout":[86],"in":[87],"neural":[89],"network.":[90],"Experiments":[91],"are":[92],"conducted":[93],"tasks,":[97],"namely":[98],"Chinese":[99],"word":[100],"segmentation,":[101],"part-of-speech":[102],"dependency":[105],"Applying":[107],"proper":[108],"methods":[110],"or":[111],"their":[112],"combinations,":[113],"error":[115],"reductions":[116],"with":[117],"respect":[118],"averaged":[121],"some":[124],"these":[126],"can":[128],"be":[129],"up":[130],"10%.":[132]},"counts_by_year":[{"year":2015,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
