{"id":"https://openalex.org/W2536430016","doi":"https://doi.org/10.1109/nlpke.2011.6138178","title":"Parsing-based Chinese word segmentation integrating morphological and syntactic information","display_name":"Parsing-based Chinese word segmentation integrating morphological and syntactic information","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2536430016","doi":"https://doi.org/10.1109/nlpke.2011.6138178","mag":"2536430016"},"language":"en","primary_location":{"id":"doi:10.1109/nlpke.2011.6138178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nlpke.2011.6138178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 7th International Conference on Natural Language Processing and Knowledge Engineering","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/A5084685506","display_name":"Xihong Wu","orcid":"https://orcid.org/0009-0004-5236-7469"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xihong Wu","raw_affiliation_strings":["Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101821645","display_name":"Meng Zhang","orcid":"https://orcid.org/0000-0003-0637-249X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meng Zhang","raw_affiliation_strings":["Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082208896","display_name":"Xiaojun Lin","orcid":"https://orcid.org/0000-0001-9117-7212"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Lin","raw_affiliation_strings":["Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084685506"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.5655,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.91093902,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"16","issue":null,"first_page":"114","last_page":"121"},"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":0.9995999932289124,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/treebank","display_name":"Treebank","score":0.8745811581611633},{"id":"https://openalex.org/keywords/crfs","display_name":"CRFS","score":0.8617585301399231},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8459585905075073},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7595113515853882},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7419293522834778},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.690341591835022},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6477583646774292},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.5826165080070496},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5576984882354736},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4816957414150238},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4601064920425415},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.4238257110118866},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10328701138496399}],"concepts":[{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.8745811581611633},{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.8617585301399231},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8459585905075073},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7595113515853882},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7419293522834778},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.690341591835022},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6477583646774292},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.5826165080070496},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5576984882354736},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4816957414150238},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4601064920425415},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.4238257110118866},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10328701138496399},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nlpke.2011.6138178","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nlpke.2011.6138178","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 7th International Conference on Natural Language Processing and Knowledge Engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W46719799","https://openalex.org/W108437174","https://openalex.org/W1551104980","https://openalex.org/W1567570606","https://openalex.org/W1575907248","https://openalex.org/W1810157568","https://openalex.org/W1979145089","https://openalex.org/W1986543644","https://openalex.org/W2031164055","https://openalex.org/W2041803325","https://openalex.org/W2096175520","https://openalex.org/W2097606805","https://openalex.org/W2139621418","https://openalex.org/W2145905222","https://openalex.org/W2146574666","https://openalex.org/W2147880316","https://openalex.org/W2152561660","https://openalex.org/W2154124206","https://openalex.org/W2160097208","https://openalex.org/W2252264945","https://openalex.org/W2349476442","https://openalex.org/W2394315834","https://openalex.org/W2425667873","https://openalex.org/W2467575451","https://openalex.org/W2785522575","https://openalex.org/W2788296320","https://openalex.org/W6604455236","https://openalex.org/W6632973184","https://openalex.org/W6634157045","https://openalex.org/W6634451464","https://openalex.org/W6646921185","https://openalex.org/W6680385414","https://openalex.org/W6682082992","https://openalex.org/W6682759559","https://openalex.org/W6683437210","https://openalex.org/W6691657211","https://openalex.org/W6719890437","https://openalex.org/W7054962354"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W50079190","https://openalex.org/W182104056","https://openalex.org/W2011251309","https://openalex.org/W2511246383","https://openalex.org/W3108423214","https://openalex.org/W2796133761","https://openalex.org/W3088215229","https://openalex.org/W45206245","https://openalex.org/W2340365369"],"abstract_inverted_index":{"The":[0,129],"conventional":[1],"sequence":[2],"labeling":[3],"methods":[4],"for":[5,122],"Chinese":[6,23,32,38,73,89],"word":[7,39,43,74],"segmentation":[8,44,75,139],"do":[9],"not":[10],"fully":[11,79],"utilize":[12,80],"the":[13,21,27,53,57,63,81,94,101,106,113,145,151],"linguistic":[14],"information,":[15,55],"which":[16,34,77,148],"restricts":[17],"further":[18],"improvements":[19],"of":[20,31,52,135],"performance.":[22],"morphology":[24],"intensively":[25],"investigates":[26],"constructions":[28],"and":[29,56,83,112],"usages":[30],"words,":[33],"is":[35,127],"helpful":[36],"to":[37,144,156],"segmentation.":[40],"Furthermore,":[41],"some":[42],"ambiguities":[45],"cannot":[46],"be":[47],"resolved":[48],"only":[49],"by":[50],"means":[51],"lexical":[54],"final":[58],"disambiguations":[59],"take":[60],"place":[61],"in":[62,138],"parsing":[64],"process.":[65],"In":[66],"this":[67],"paper,":[68],"we":[69],"propose":[70],"a":[71,116],"parsing-based":[72,110],"model,":[76,115,147],"can":[78],"morphological":[82],"syntactic":[84],"information.":[85],"Experiments":[86],"on":[87],"Penn":[88],"Treebank(CTB)":[90],"5.0":[91],"show":[92],"that":[93,150],"proposed":[95],"model":[96,111,119,131],"obtains":[97,132],"competitive":[98],"performances":[99],"as":[100],"CRFs-based":[102,114,146],"model.":[103],"To":[104],"investigate":[105],"relationship":[107],"between":[108],"our":[109],"maximum":[117],"entropy":[118],"based":[120],"framework":[121],"integrating":[123,130],"different":[124],"knowledge":[125],"sources":[126],"employed.":[128],"an":[133],"F-measure":[134],"97.9,":[136],"25%":[137],"error":[140],"rate":[141],"reduction":[142],"relative":[143],"indicates":[149],"two":[152],"models":[153],"are":[154],"complementary":[155],"each":[157],"other.":[158]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}