{"id":"https://openalex.org/W2978801638","doi":"https://doi.org/10.13053/cys-23-3-3278","title":"Text Classification using Gated Fusion of n-gram Features and Semantic Features","display_name":"Text Classification using Gated Fusion of n-gram Features and Semantic Features","publication_year":2019,"publication_date":"2019-10-07","ids":{"openalex":"https://openalex.org/W2978801638","doi":"https://doi.org/10.13053/cys-23-3-3278","mag":"2978801638"},"language":"en","primary_location":{"id":"doi:10.13053/cys-23-3-3278","is_oa":false,"landing_page_url":"https://doi.org/10.13053/cys-23-3-3278","pdf_url":null,"source":{"id":"https://openalex.org/S61446325","display_name":"Computaci\u00f3n y Sistemas","issn_l":"1405-5546","issn":["1405-5546","2007-9737"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319599","host_organization_name":"National Polytechnic Institute","host_organization_lineage":["https://openalex.org/P4310319599"],"host_organization_lineage_names":["National Polytechnic Institute"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computaci\u00f3n y Sistemas","raw_type":"journal-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/A5017825948","display_name":"Ajay Nagar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ajay Nagar","raw_affiliation_strings":["Samsung R&D Institute India - Bangalore (India)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute India - Bangalore (India)","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038091890","display_name":"Anmol Bhasin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anmol Bhasin","raw_affiliation_strings":["Samsung R&D Institute India - Bangalore (India)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute India - Bangalore (India)","institution_ids":["https://openalex.org/I4210139030"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058417945","display_name":"Gaurav Mathur","orcid":"https://orcid.org/0000-0002-7941-9538"},"institutions":[{"id":"https://openalex.org/I4210139030","display_name":"Samsung (India)","ror":"https://ror.org/04cpx2569","country_code":"IN","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210139030"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gaurav Mathur","raw_affiliation_strings":["Samsung R&D Institute India - Bangalore (India)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Samsung R&D Institute India - Bangalore (India)","institution_ids":["https://openalex.org/I4210139030"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1446,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.58907309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"23","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing 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"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9994999766349792,"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":0.7667880058288574},{"id":"https://openalex.org/keywords/n-gram","display_name":"n-gram","score":0.7557854056358337},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6520678997039795},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5887295603752136},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5846098065376282},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5774229764938354},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5715664625167847},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.5480483174324036},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4306011497974396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3847798705101013},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.20754092931747437},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.20072871446609497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7667880058288574},{"id":"https://openalex.org/C117884012","wikidata":"https://www.wikidata.org/wiki/Q94489","display_name":"n-gram","level":3,"score":0.7557854056358337},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6520678997039795},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5887295603752136},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5846098065376282},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5774229764938354},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5715664625167847},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5480483174324036},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4306011497974396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3847798705101013},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.20754092931747437},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.20072871446609497},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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":1,"locations":[{"id":"doi:10.13053/cys-23-3-3278","is_oa":false,"landing_page_url":"https://doi.org/10.13053/cys-23-3-3278","pdf_url":null,"source":{"id":"https://openalex.org/S61446325","display_name":"Computaci\u00f3n y Sistemas","issn_l":"1405-5546","issn":["1405-5546","2007-9737"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319599","host_organization_name":"National Polytechnic Institute","host_organization_lineage":["https://openalex.org/P4310319599"],"host_organization_lineage_names":["National Polytechnic Institute"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computaci\u00f3n y Sistemas","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2153579005","https://openalex.org/W2950577311","https://openalex.org/W2964308564","https://openalex.org/W2964331270"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W2086519370","https://openalex.org/W2028665553","https://openalex.org/W2087343574","https://openalex.org/W2535915176","https://openalex.org/W2105860728"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2],"novel":[3],"method":[4,87],"for":[5,43,97],"text":[6,98],"classification":[7,99],"based":[8,32],"on":[9,33,67,100],"gated":[10],"fusion":[11,54],"of":[12,18],"n-gram":[13,27,59],"features":[14,17],"and":[15,60,76,82],"semantic":[16,44,61],"the":[19,26,30,34,92],"text.":[20],"The":[21,63,85],"parallel":[22],"CNN":[23],"network":[24],"captures":[25],"relation":[28],"between":[29],"words":[31],"filter":[35],"size,":[36],"primarily":[37],"short":[38],"distance":[39],"multi-word":[40],"relations.":[41],"Whereas":[42],"relation-ship,":[45],"universal":[46],"sentence":[47],"encoder":[48],"or":[49],"BiLSTM":[50],"is":[51,55,65,88],"used.":[52],"Gated":[53],"used":[56,70],"to":[57,90],"combine":[58],"features.":[62],"model":[64],"evaluated":[66],"4":[68],"commonly":[69],"benchmark":[71],"datasets":[72],"(MR,":[73],"TREC,":[74],"AG-News":[75],"SUBJ),":[77],"which":[78],"includes":[79],"sentiment":[80],"analysis":[81],"question":[83],"classification.":[84],"proposed":[86],"able":[89],"surpass":[91],"existing":[93],"state-of-the-art":[94],"DNN":[95],"architectures":[96],"these":[101],"datasets.":[102]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
