{"id":"https://openalex.org/W2251908724","doi":"https://doi.org/10.18653/v1/d15-1093","title":"Learning Timeline Difference for Text Categorization","display_name":"Learning Timeline Difference for Text Categorization","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2251908724","doi":"https://doi.org/10.18653/v1/d15-1093","mag":"2251908724"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d15-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1093","pdf_url":"https://www.aclweb.org/anthology/D15-1093.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 2015 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://www.aclweb.org/anthology/D15-1093.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079605672","display_name":"Fumiyo Fukumoto","orcid":"https://orcid.org/0000-0001-7858-6206"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Fumiyo Fukumoto","raw_affiliation_strings":["Graduate Faculty of Interdisciplinary Research Univ. of Yamanashi, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate Faculty of Interdisciplinary Research Univ. of Yamanashi, Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003025940","display_name":"Yoshimi Suzuki","orcid":"https://orcid.org/0000-0001-5466-7351"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshimi Suzuki","raw_affiliation_strings":["Graduate Faculty of Interdisciplinary Research Univ. of Yamanashi, Japan"],"affiliations":[{"raw_affiliation_string":"Graduate Faculty of Interdisciplinary Research Univ. of Yamanashi, Japan","institution_ids":["https://openalex.org/I66906201"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079605672"],"corresponding_institution_ids":["https://openalex.org/I66906201"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08655668,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"799","last_page":"804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","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"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","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/T12535","display_name":"Machine Learning and Data Classification","score":0.9926000237464905,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9835000038146973,"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/timeline","display_name":"Timeline","score":0.980042576789856},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.8154619336128235},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7861635684967041},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7343748211860657},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6052992343902588},{"id":"https://openalex.org/keywords/text-categorization","display_name":"Text categorization","score":0.589257538318634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5878381729125977},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5076545476913452},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4414907693862915},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11926347017288208},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09228858351707458}],"concepts":[{"id":"https://openalex.org/C4438859","wikidata":"https://www.wikidata.org/wiki/Q186117","display_name":"Timeline","level":2,"score":0.980042576789856},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.8154619336128235},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7861635684967041},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343748211860657},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6052992343902588},{"id":"https://openalex.org/C2986744138","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Text categorization","level":3,"score":0.589257538318634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5878381729125977},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5076545476913452},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4414907693862915},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11926347017288208},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09228858351707458},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.18653/v1/d15-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1093","pdf_url":"https://www.aclweb.org/anthology/D15-1093.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 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.697.1114","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.1114","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/D15/D15-1093.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/d15-1093","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d15-1093","pdf_url":"https://www.aclweb.org/anthology/D15-1093.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 2015 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251908724.pdf","grobid_xml":"https://content.openalex.org/works/W2251908724.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W22861983","https://openalex.org/W85561527","https://openalex.org/W91242194","https://openalex.org/W1593041439","https://openalex.org/W1907380269","https://openalex.org/W1915315806","https://openalex.org/W1970191588","https://openalex.org/W1982263504","https://openalex.org/W1988790447","https://openalex.org/W2009136420","https://openalex.org/W2026302857","https://openalex.org/W2068463433","https://openalex.org/W2083619974","https://openalex.org/W2104471998","https://openalex.org/W2118585731","https://openalex.org/W2120354757","https://openalex.org/W2122838776","https://openalex.org/W2123958887","https://openalex.org/W2129006692","https://openalex.org/W2134510195","https://openalex.org/W2145241906","https://openalex.org/W2147179108","https://openalex.org/W2158108973","https://openalex.org/W2165744911","https://openalex.org/W2170895986","https://openalex.org/W2435251607","https://openalex.org/W3146885639","https://openalex.org/W4245132200"],"related_works":["https://openalex.org/W2150617187","https://openalex.org/W2155449793","https://openalex.org/W1529840045","https://openalex.org/W4244036394","https://openalex.org/W1842879116","https://openalex.org/W2135107501","https://openalex.org/W2047248895","https://openalex.org/W1822895636","https://openalex.org/W1557517194","https://openalex.org/W3199244114"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"text":[3],"categorization":[4],"problem":[5],"that":[6,40],"training":[7,69],"data":[8,64],"may":[9],"derive":[10],"from":[11,16,67],"a":[12,22,27],"different":[13],"time":[14,59],"period":[15,60],"the":[17,41,46,52,57,62,68],"test":[18,63],"data.":[19,70],"We":[20],"present":[21],"learning":[23],"framework":[24],"which":[25],"extends":[26],"boosting":[28],"technique":[29],"to":[30,45],"learn":[31],"accurate":[32],"model":[33],"for":[34],"timeline":[35],"adaptation.":[36],"The":[37],"results":[38],"showed":[39],"method":[42,53],"was":[43],"comparable":[44],"current":[47],"state-of-theart":[48],"biased-SVM":[49],"method,":[50],"especially":[51],"is":[54],"effective":[55],"when":[56],"creation":[58],"of":[61],"differs":[65],"greatly":[66]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
