{"id":"https://openalex.org/W4313306935","doi":"https://doi.org/10.1109/iccais56082.2022.9990092","title":"A Keyphrase Extraction Method Based on Multi-feature Evaluation and Mask Mechanism","display_name":"A Keyphrase Extraction Method Based on Multi-feature Evaluation and Mask Mechanism","publication_year":2022,"publication_date":"2022-11-21","ids":{"openalex":"https://openalex.org/W4313306935","doi":"https://doi.org/10.1109/iccais56082.2022.9990092"},"language":"en","primary_location":{"id":"doi:10.1109/iccais56082.2022.9990092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais56082.2022.9990092","pdf_url":null,"source":{"id":"https://openalex.org/S4363608617","display_name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5025348966","display_name":"Liwen Ma","orcid":"https://orcid.org/0000-0002-1286-2121"},"institutions":[{"id":"https://openalex.org/I51622183","display_name":"Shaanxi University of Science and Technology","ror":"https://ror.org/034t3zs45","country_code":"CN","type":"education","lineage":["https://openalex.org/I51622183"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwen Ma","raw_affiliation_strings":["Shaanxi University of Science and Technology,School of Electrical and Control Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi University of Science and Technology,School of Electrical and Control Engineering,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I51622183"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100444142","display_name":"Weifeng Liu","orcid":"https://orcid.org/0000-0001-9537-8767"},"institutions":[{"id":"https://openalex.org/I51622183","display_name":"Shaanxi University of Science and Technology","ror":"https://ror.org/034t3zs45","country_code":"CN","type":"education","lineage":["https://openalex.org/I51622183"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifeng Liu","raw_affiliation_strings":["Shaanxi University of Science and Technology,School of Electrical and Control Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shaanxi University of Science and Technology,School of Electrical and Control Engineering,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I51622183"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51622183"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18333945,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"44","issue":null,"first_page":"164","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis 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/T13083","display_name":"Advanced Text Analysis 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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8588228225708008},{"id":"https://openalex.org/keywords/phrase","display_name":"Phrase","score":0.746408224105835},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6296678781509399},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6026817560195923},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5432087779045105},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5084823369979858},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48457634449005127},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10736879706382751}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8588228225708008},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.746408224105835},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6296678781509399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6026817560195923},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5432087779045105},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5084823369979858},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48457634449005127},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10736879706382751},{"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/iccais56082.2022.9990092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccais56082.2022.9990092","pdf_url":null,"source":{"id":"https://openalex.org/S4363608617","display_name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1828830618","https://openalex.org/W1880262756","https://openalex.org/W2076470073","https://openalex.org/W2167329753","https://openalex.org/W2332132381","https://openalex.org/W2740811004","https://openalex.org/W2782177496","https://openalex.org/W2792059528","https://openalex.org/W2890179025","https://openalex.org/W2913961733","https://openalex.org/W2962903510","https://openalex.org/W2963245897","https://openalex.org/W2963345057","https://openalex.org/W2964656262","https://openalex.org/W2973226110","https://openalex.org/W2974528752","https://openalex.org/W3000623537","https://openalex.org/W3086979146","https://openalex.org/W3102838711","https://openalex.org/W3175716528","https://openalex.org/W3213754079","https://openalex.org/W4237040408","https://openalex.org/W6601310731","https://openalex.org/W6631501603","https://openalex.org/W6638437678","https://openalex.org/W6683944584","https://openalex.org/W6743384090"],"related_works":["https://openalex.org/W2039546652","https://openalex.org/W2012262991","https://openalex.org/W2373794620","https://openalex.org/W2060629350","https://openalex.org/W2357294589","https://openalex.org/W2386861027","https://openalex.org/W2349302580","https://openalex.org/W2390154576","https://openalex.org/W2916983164","https://openalex.org/W2296205523"],"abstract_inverted_index":{"Keyphrase":[0],"extraction":[1,16],"aims":[2],"to":[3,20,27,32],"identify":[4],"phrases":[5],"in":[6],"documents":[7],"that":[8,129],"contain":[9],"core":[10],"content.":[11],"However,":[12],"existing":[13,124],"unsupervised":[14],"keyphrase":[15,38,56],"models":[17],"are":[18,95,110],"limited":[19],"focusing":[21],"on":[22,112],"a":[23,58],"single":[24],"feature":[25],"leading":[26],"biased":[28],"results.":[29],"In":[30],"response":[31],"the":[33,54,61,65,69,75,82,92,98,106,138],"above":[34],"problems,":[35],"it":[36,52,80],"evaluates":[37],"scores":[39],"through":[40],"multiple":[41,134],"features":[42,94,135],"of":[43,85,140],"semantic":[44,76],"importance,":[45],"topic":[46,89],"diversity,":[47,90],"and":[48,60,68,91,116],"position":[49,93],"features.":[50],"Firstly,":[51],"masked":[53],"candidate":[55,86],"from":[57,133],"document":[59,67,71],"Manhattan":[62],"distance":[63],"between":[64],"mask":[66],"original":[70],"is":[72,102],"calculated":[73,81,103],"as":[74,88],"importance":[77,100,132],"feature.":[78],"Secondly,":[79],"topic-word":[83],"distribution":[84],"keyphrases":[87],"calculated.":[96],"Finally,":[97],"phrase":[99,131],"score":[101],"by":[104],"integrating":[105],"three":[107,113],"sub-models.":[108],"Experiments":[109],"conducted":[111],"academic":[114],"datasets":[115],"compared":[117],"with":[118],"six":[119],"state-of-the-art":[120],"baseline":[121],"models,":[122],"outperforming":[123],"methods.":[125],"The":[126],"results":[127],"show":[128],"evaluating":[130],"significantly":[136],"improves":[137],"performance":[139],"extracting":[141],"keyphrases.":[142]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
