{"id":"https://openalex.org/W2159465963","doi":"https://doi.org/10.3115/1218955.1219036","title":"A kernel PCA method for superior word sense disambiguation","display_name":"A kernel PCA method for superior word sense disambiguation","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W2159465963","doi":"https://doi.org/10.3115/1218955.1219036","mag":"2159465963"},"language":"en","primary_location":{"id":"doi:10.3115/1218955.1219036","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1218955.1219036","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1218955.1219036","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics  - ACL '04","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.3115/1218955.1219036","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083694186","display_name":"Dekai Wu","orcid":"https://orcid.org/0000-0001-9833-361X"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Dekai Wu","raw_affiliation_strings":["University of Science and Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060723223","display_name":"Weifeng Su","orcid":"https://orcid.org/0000-0001-9501-0321"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Weifeng Su","raw_affiliation_strings":["University of Science and Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078390032","display_name":"Marine Carpuat","orcid":"https://orcid.org/0000-0003-1693-0782"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Marine Carpuat","raw_affiliation_strings":["University of Science and Technology, Hong Kong"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology, Hong Kong","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083694186"],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":5.0834,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.95333017,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"637","last_page":"es"},"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":0.9994000196456909,"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/T11550","display_name":"Text and Document Classification Technologies","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8014878034591675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7681052684783936},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.7083128690719604},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6679106950759888},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5351575016975403},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5333538055419922},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.48425909876823425},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.47831395268440247},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4467419683933258},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4238382875919342},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3641641438007355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12456592917442322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8014878034591675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7681052684783936},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.7083128690719604},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6679106950759888},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5351575016975403},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5333538055419922},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.48425909876823425},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.47831395268440247},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4467419683933258},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4238382875919342},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3641641438007355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12456592917442322},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3115/1218955.1219036","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1218955.1219036","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1218955.1219036","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics  - ACL '04","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.64.2333","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.2333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.ust.hk/~dekai/library/WU_Dekai/WuSuCarpuat_ACL2004.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.3115/1218955.1219036","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1218955.1219036","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1218955.1219036","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics  - ACL '04","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6200000047683716,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2159465963.pdf","grobid_xml":"https://content.openalex.org/works/W2159465963.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W740415","https://openalex.org/W3851596","https://openalex.org/W12297830","https://openalex.org/W19723716","https://openalex.org/W56012926","https://openalex.org/W143727535","https://openalex.org/W1542641025","https://openalex.org/W1555711139","https://openalex.org/W1567165223","https://openalex.org/W1795234945","https://openalex.org/W1798097800","https://openalex.org/W1967350923","https://openalex.org/W1983073929","https://openalex.org/W1995945562","https://openalex.org/W2094618127","https://openalex.org/W2104029044","https://openalex.org/W2140095548","https://openalex.org/W2149684865","https://openalex.org/W2153728818","https://openalex.org/W2156909104","https://openalex.org/W2159859968","https://openalex.org/W2797062063"],"related_works":["https://openalex.org/W2543923804","https://openalex.org/W2118232727","https://openalex.org/W1984421104","https://openalex.org/W2512565647","https://openalex.org/W1552109592","https://openalex.org/W2073682124","https://openalex.org/W2398887903","https://openalex.org/W2127229869","https://openalex.org/W2222829538","https://openalex.org/W2004465977"],"abstract_inverted_index":{"We":[0,29,53],"introduce":[1],"a":[2,11],"new":[3],"method":[4],"for":[5,89],"disambiguating":[6],"word":[7],"senses":[8],"that":[9,70,81],"exploits":[10],"nonlinear":[12],"Kernel":[13],"Principal":[14],"Component":[15],"Analysis":[16],"(KPCA)":[17],"technique":[18],"to":[19,23,38],"achieve":[20],"accuracy":[21,36],"superior":[22],"the":[24,39,62,75],"best":[25],"published":[26],"individual":[27],"models.":[28],"present":[30],"empirical":[31],"results":[32,86],"demonstrating":[33],"significantly":[34],"better":[35],"compared":[37],"state-of-the-art":[40],"achieved":[41],"by":[42],"either":[43],"na\u00efve":[44],"Bayes":[45],"or":[46],"maximum":[47],"entropy":[48],"models,":[49],"on":[50,87],"Senseval-2":[51],"data.":[52],"also":[54],"contrast":[55],"against":[56],"another":[57],"type":[58],"of":[59,98],"kernel":[60],"method,":[61],"support":[63],"vector":[64],"machine":[65],"(SVM)":[66],"model,":[67],"and":[68],"show":[69],"our":[71],"KPCA-based":[72],"model":[73],"outperforms":[74],"SVM-based":[76],"model.":[77],"It":[78],"is":[79],"hoped":[80],"these":[82,99],"highly":[83],"encouraging":[84],"first":[85],"KPCA":[88],"natural":[90],"language":[91],"processing":[92],"tasks":[93],"will":[94],"inspire":[95],"further":[96],"development":[97],"directions.":[100]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}