{"id":"https://openalex.org/W2530794000","doi":"https://doi.org/10.3115/v1/e14-1027","title":"Incremental Bayesian Learning of Semantic Categories","display_name":"Incremental Bayesian Learning of Semantic Categories","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W2530794000","doi":"https://doi.org/10.3115/v1/e14-1027","mag":"2530794000"},"language":"en","primary_location":{"id":"doi:10.3115/v1/e14-1027","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/e14-1027","pdf_url":"https://aclanthology.org/E14-1027.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 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://aclanthology.org/E14-1027.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025156794","display_name":"Lea Frermann","orcid":"https://orcid.org/0000-0002-9712-1188"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lea Frermann","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041024491","display_name":"Mirella Lapata","orcid":"https://orcid.org/0000-0002-2107-1516"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mirella Lapata","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025156794"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6914,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88682051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"249","last_page":"258"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9976000189781189,"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/T10028","display_name":"Topic Modeling","score":0.9976000189781189,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9950000047683716,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9890000224113464,"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/computer-science","display_name":"Computer science","score":0.7465316653251648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6736747026443481},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6360123157501221},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.6294705867767334},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.614942193031311},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5219954252243042},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5137702226638794},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4897634983062744},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.48658668994903564},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4720761775970459},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4476383328437805},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.43225857615470886},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4221609830856323},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.42047643661499023},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1509992778301239},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1230214536190033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7465316653251648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6736747026443481},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6360123157501221},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.6294705867767334},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.614942193031311},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5219954252243042},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5137702226638794},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4897634983062744},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.48658668994903564},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4720761775970459},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4476383328437805},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.43225857615470886},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4221609830856323},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.42047643661499023},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1509992778301239},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1230214536190033},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3115/v1/e14-1027","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/e14-1027","pdf_url":"https://aclanthology.org/E14-1027.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 14th Conference of the European Chapter of the Association for Computational Linguistics","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/7cf97392-f328-4c7b-b136-72c61ecfc8b5","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/7cf97392-f328-4c7b-b136-72c61ecfc8b5","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frermann, L & Lapata, M 2014, Incremental Bayesian Learning of Semantic Categories. in Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. Gothenburg, Sweden, pp. 249-258. < http://www.aclweb.org/anthology/E14-1027 >","raw_type":"contributionToPeriodical"},{"id":"pmh:oai:pure.ed.ac.uk:publications/7cf97392-f328-4c7b-b136-72c61ecfc8b5","is_oa":false,"landing_page_url":"http://hdl.handle.net/20.500.11820/7cf97392-f328-4c7b-b136-72c61ecfc8b5","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"doi:10.3115/v1/e14-1027","is_oa":true,"landing_page_url":"https://doi.org/10.3115/v1/e14-1027","pdf_url":"https://aclanthology.org/E14-1027.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 14th Conference of the European Chapter of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8799999952316284,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2530794000.pdf","grobid_xml":"https://content.openalex.org/works/W2530794000.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W367043469","https://openalex.org/W1483307070","https://openalex.org/W1513896028","https://openalex.org/W1534993021","https://openalex.org/W1580018148","https://openalex.org/W1842705192","https://openalex.org/W1880262756","https://openalex.org/W1974578407","https://openalex.org/W1987302197","https://openalex.org/W1987959440","https://openalex.org/W1988983983","https://openalex.org/W1990907758","https://openalex.org/W2002654918","https://openalex.org/W2016939656","https://openalex.org/W2020999234","https://openalex.org/W2026999222","https://openalex.org/W2056199156","https://openalex.org/W2065251543","https://openalex.org/W2067242602","https://openalex.org/W2072588367","https://openalex.org/W2078894097","https://openalex.org/W2080943198","https://openalex.org/W2086618114","https://openalex.org/W2109291777","https://openalex.org/W2111024941","https://openalex.org/W2115056808","https://openalex.org/W2130363011","https://openalex.org/W2138615112","https://openalex.org/W2141545068","https://openalex.org/W2146485022","https://openalex.org/W2154164802","https://openalex.org/W2159740205","https://openalex.org/W2171566979","https://openalex.org/W2250252080","https://openalex.org/W2579149292","https://openalex.org/W2619231787","https://openalex.org/W2882319491","https://openalex.org/W4299947740"],"related_works":["https://openalex.org/W2372267530","https://openalex.org/W2969189870","https://openalex.org/W2965643117","https://openalex.org/W4303857162","https://openalex.org/W2407375987","https://openalex.org/W3049691116","https://openalex.org/W2505726097","https://openalex.org/W2010643158","https://openalex.org/W2106867672","https://openalex.org/W3081214562"],"abstract_inverted_index":{"Models":[0],"of":[1,40],"category":[2],"learning":[3],"have":[4],"been":[5],"extensively":[6],"studied":[7],"in":[8,59,79],"cognitive":[9,99],"science":[10],"and":[11,56,97],"primarily":[12],"tested":[13],"on":[14,25],"perceptual":[15],"abstractions":[16],"or":[17],"artificial":[18],"stimuli.":[19],"In":[20],"this":[21],"paper":[22],"we":[23],"focus":[24],"categories":[26,55,96],"acquired":[27],"from":[28],"natural":[29],"language":[30],"stimuli,":[31],"that":[32,90],"is":[33,37],"words":[34],"(e.g.,":[35],"chair":[36],"a":[38,46,60,68,85],"member":[39],"the":[41],"FURNITURE":[42],"category).":[43],"We":[44],"present":[45],"Bayesian":[47],"model":[48,64,92],"which,":[49],"unlike":[50],"previous":[51],"work,":[52],"learns":[53,93],"both":[54],"their":[57],"features":[58],"single":[61],"process.":[62],"Our":[63],"employs":[65],"particle":[66],"filters,":[67],"sequential":[69],"Monte":[70],"Carlo":[71],"method":[72],"commonly":[73],"used":[74],"for":[75],"approximate":[76],"probabilistic":[77],"inference":[78],"an":[80],"incremental":[81],"setting.":[82],"Comparison":[83],"against":[84],"state-of-the-art":[86],"graph-based":[87],"approach":[88],"reveals":[89],"our":[91],"qualitatively":[94],"better":[95],"demonstrates":[98],"plausibility":[100],"during":[101],"learning.":[102]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}