{"id":"https://openalex.org/W2782177496","doi":"https://doi.org/10.1609/aaai.v32i1.12144","title":"Learning Feature Representations for Keyphrase Extraction","display_name":"Learning Feature Representations for Keyphrase Extraction","publication_year":2018,"publication_date":"2018-04-29","ids":{"openalex":"https://openalex.org/W2782177496","doi":"https://doi.org/10.1609/aaai.v32i1.12144","mag":"2782177496"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v32i1.12144","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.12144","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v32i1.12144","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077106860","display_name":"Corina Florescu","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Corina Florescu","raw_affiliation_strings":["University of North Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Texas","institution_ids":["https://openalex.org/I123534392"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111486488","display_name":"Wei Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I123534392","display_name":"University of North Texas","ror":"https://ror.org/00v97ad02","country_code":"US","type":"education","lineage":["https://openalex.org/I123534392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Jin","raw_affiliation_strings":["University of North Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Texas","institution_ids":["https://openalex.org/I123534392"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I123534392"],"apc_list":null,"apc_paid":null,"fwci":0.7923,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.7674862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"32","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis 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/T13083","display_name":"Advanced Text Analysis 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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.884419322013855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6443687081336975},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.6160966157913208},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5670808553695679},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5495081543922424},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5311101078987122},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5178287625312805},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5069928169250488},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4937666356563568},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4890464246273041},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47331225872039795},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.43110793828964233},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35264450311660767},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1520816683769226},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.059371113777160645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.884419322013855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6443687081336975},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.6160966157913208},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5670808553695679},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5495081543922424},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5311101078987122},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5178287625312805},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5069928169250488},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4937666356563568},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4890464246273041},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47331225872039795},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.43110793828964233},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35264450311660767},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1520816683769226},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.059371113777160645},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1609/aaai.v32i1.12144","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.12144","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:ojs.aaai.org:article/12144","is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/12144","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/12144/12003","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v32i1.12144","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v32i1.12144","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G4551277117","display_name":"CAREER: Creation, Visualization, and Mining of Domain Textual Graphs: Integrating Domain Knowledge and Human Intelligence","funder_award_id":"1739095","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W32253530","https://openalex.org/W1907578970","https://openalex.org/W2030903088","https://openalex.org/W2064418625","https://openalex.org/W2141599568","https://openalex.org/W2154851992","https://openalex.org/W2251009376","https://openalex.org/W2740811004","https://openalex.org/W3104097132","https://openalex.org/W6639689704","https://openalex.org/W6691383283","https://openalex.org/W6948116018"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W3199964822","https://openalex.org/W4232132981","https://openalex.org/W4238046985","https://openalex.org/W3164948662","https://openalex.org/W3003242282","https://openalex.org/W3153597579","https://openalex.org/W3012824888","https://openalex.org/W2998249245"],"abstract_inverted_index":{"In":[0,55],"supervised":[1],"approaches":[2],"for":[3],"keyphrase":[4],"extraction,":[5],"a":[6,12,42,61,83],"candidate":[7],"phrase":[8],"is":[9,41],"encoded":[10],"with":[11],"set":[13],"of":[14,90],"hand-crafted":[15],"features":[16,31],"and":[17,49,85],"machine":[18],"learning":[19,63],"algorithms":[20],"are":[21],"trained":[22],"to":[23,34,70],"discriminate":[24],"keyphrases":[25,75],"from":[26],"non-keyphrases.":[27],"Although":[28],"the":[29,67,80],"manually-designed":[30],"have":[32],"shown":[33],"work":[35],"well":[36],"in":[37,98],"practice,":[38],"feature":[39,62,88],"engineering":[40],"difficult":[43],"process":[44],"that":[45,65,74],"requires":[46],"expert":[47],"knowledge":[48],"normally":[50],"does":[51],"not":[52],"generalize":[53],"well.":[54],"this":[56],"paper,":[57],"we":[58],"present":[59],"SurfKE,":[60],"framework":[64],"exploits":[66],"text":[68],"itself":[69],"automatically":[71,86],"discover":[72],"patterns":[73],"exhibit.":[76],"Our":[77],"model":[78,94],"represents":[79],"document":[81],"as":[82],"graph":[84],"learns":[87],"representation":[89],"phrases.":[91],"The":[92],"proposed":[93],"obtains":[95],"remarkable":[96],"improvements":[97],"performance":[99],"over":[100],"strong":[101],"baselines.":[102]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}