{"id":"https://openalex.org/W4386514354","doi":"https://doi.org/10.1145/3587716.3587972","title":"A Lightweight Keyword Extraction Algorithm Using a Term Weighting Scheme with Word Features","display_name":"A Lightweight Keyword Extraction Algorithm Using a Term Weighting Scheme with Word Features","publication_year":2023,"publication_date":"2023-02-17","ids":{"openalex":"https://openalex.org/W4386514354","doi":"https://doi.org/10.1145/3587716.3587972"},"language":"en","primary_location":{"id":"doi:10.1145/3587716.3587972","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3587716.3587972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"},"type":"article","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/A5008213060","display_name":"Santipong Thaiprayoon","orcid":"https://orcid.org/0000-0003-3021-474X"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"FernUniversit\u00e4t in Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Santipong Thaiprayoon","raw_affiliation_strings":["Department of Communication Networks, FernUniversit\u00e4t in Hagen, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3021-474X","affiliations":[{"raw_affiliation_string":"Department of Communication Networks, FernUniversit\u00e4t in Hagen, Germany","institution_ids":["https://openalex.org/I120691247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003105685","display_name":"Herwig Unger","orcid":"https://orcid.org/0000-0002-8818-3600"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"FernUniversit\u00e4t in Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Herwig Unger","raw_affiliation_strings":["Department of Communication Networks, FernUniversit\u00e4t in Hagen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-8818-3600","affiliations":[{"raw_affiliation_string":"Department of Communication Networks, FernUniversit\u00e4t in Hagen, Germany","institution_ids":["https://openalex.org/I120691247"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008213060"],"corresponding_institution_ids":["https://openalex.org/I120691247"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11141428,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":null,"first_page":"602","last_page":"606"},"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/weighting","display_name":"Weighting","score":0.8164380788803101},{"id":"https://openalex.org/keywords/keyword-extraction","display_name":"Keyword extraction","score":0.8080741763114929},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7811977863311768},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.7549645900726318},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.6892328858375549},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.617722749710083},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4305305480957031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4180288016796112},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33581632375717163},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3354584276676178},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3354407846927643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1048547625541687}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.8164380788803101},{"id":"https://openalex.org/C2780288562","wikidata":"https://www.wikidata.org/wiki/Q25053353","display_name":"Keyword extraction","level":2,"score":0.8080741763114929},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7811977863311768},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7549645900726318},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.6892328858375549},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.617722749710083},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4305305480957031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4180288016796112},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33581632375717163},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3354584276676178},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3354407846927643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1048547625541687},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3587716.3587972","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1145/3587716.3587972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 15th International Conference on Machine Learning and Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2056909389","https://openalex.org/W2064418625","https://openalex.org/W2889542933","https://openalex.org/W2962798918","https://openalex.org/W2964059932","https://openalex.org/W2973226110","https://openalex.org/W2993506183","https://openalex.org/W3009360413","https://openalex.org/W3009722049","https://openalex.org/W3102838711","https://openalex.org/W3149374825","https://openalex.org/W3153904578","https://openalex.org/W3166157155","https://openalex.org/W3184133963","https://openalex.org/W4206828928","https://openalex.org/W4236778376"],"related_works":["https://openalex.org/W2276271130","https://openalex.org/W2367393702","https://openalex.org/W2388798633","https://openalex.org/W2521881570","https://openalex.org/W2043505748","https://openalex.org/W4243530889","https://openalex.org/W1978394996","https://openalex.org/W2088384101","https://openalex.org/W159132833","https://openalex.org/W1593472062"],"abstract_inverted_index":{"The":[0,49,68],"rapid":[1],"growth":[2],"of":[3,6,88],"numerous":[4],"collections":[5],"unstructured":[7,60],"text":[8,61],"increases":[9],"the":[10,77],"need":[11],"to":[12,52],"extract":[13,54],"meaningful":[14],"information.":[15],"This":[16],"paper":[17],"proposes":[18],"a":[19,27],"new":[20],"lightweight":[21],"keyword":[22],"extraction":[23],"algorithm":[24,79],"based":[25],"on":[26,71],"term":[28,36,41,44,47],"weighting":[29],"scheme":[30],"with":[31],"multiple":[32],"word":[33],"features,":[34],"including":[35],"frequency,":[37,40],"inverse":[38],"sentence":[39],"difference":[42],"sentence,":[43],"position,":[45],"and":[46,57,83],"length.":[48],"goal":[50],"is":[51],"automatically":[53],"important":[55],"words":[56],"phrases":[58],"from":[59],"without":[62],"training":[63],"data":[64],"or":[65],"domain-specific":[66],"knowledge.":[67],"experimental":[69],"results":[70],"several":[72],"benchmark":[73],"datasets":[74],"show":[75],"that":[76],"proposed":[78],"significantly":[80],"outperforms":[81],"baseline":[82],"state-of-the-art":[84],"approaches":[85],"in":[86],"terms":[87],"F1":[89],"scores.":[90]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}