{"id":"https://openalex.org/W2949454212","doi":"https://doi.org/10.18653/v1/p19-2052","title":"De-Mixing Sentiment from Code-Mixed Text","display_name":"De-Mixing Sentiment from Code-Mixed Text","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2949454212","doi":"https://doi.org/10.18653/v1/p19-2052","mag":"2949454212"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-2052","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2052","pdf_url":"https://www.aclweb.org/anthology/P19-2052.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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-2052.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064437096","display_name":"Yash Kumar Lal","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yash Kumar Lal","raw_affiliation_strings":["Johns Hopkins University,"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103910723","display_name":"Vaibhav Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I1299907687","display_name":"Bloomberg (United States)","ror":"https://ror.org/02rdpzb15","country_code":"US","type":"company","lineage":["https://openalex.org/I1299907687"]},{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vaibhav Kumar","raw_affiliation_strings":["Carnegie Mellon University,  Bloomberg LP,","International Institute of Information Technology","Carnegie Mellon University, \u2022 Bloomberg LP,"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,  Bloomberg LP,","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"International Institute of Information Technology","institution_ids":[]},{"raw_affiliation_string":"Carnegie Mellon University, \u2022 Bloomberg LP,","institution_ids":["https://openalex.org/I74973139","https://openalex.org/I1299907687"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108767051","display_name":"Mrinal Kanti Dhar","orcid":"https://orcid.org/0000-0002-9976-0039"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mrinal Dhar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013675579","display_name":"Manish Shrivastava","orcid":"https://orcid.org/0000-0001-8705-6637"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manish Shrivastava","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5112315093","display_name":"Philipp Koehn","orcid":null},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philipp Koehn","raw_affiliation_strings":["Johns Hopkins University,"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064437096"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":4.9135,"has_fulltext":true,"cited_by_count":52,"citation_normalized_percentile":{"value":0.96098926,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"371","last_page":"377"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9995999932289124,"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":0.9993000030517578,"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":0.8462147116661072},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7066243886947632},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6834898591041565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6581394672393799},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5958609580993652},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5543681979179382},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5528110861778259},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5212953090667725},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5208602547645569},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.46942073106765747},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45155274868011475},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44873908162117004},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.4154200851917267},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.19468578696250916},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.18422913551330566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8462147116661072},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7066243886947632},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6834898591041565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6581394672393799},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5958609580993652},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5543681979179382},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5528110861778259},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5212953090667725},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5208602547645569},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.46942073106765747},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45155274868011475},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44873908162117004},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.4154200851917267},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.19468578696250916},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.18422913551330566},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-2052","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2052","pdf_url":"https://www.aclweb.org/anthology/P19-2052.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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-2052","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-2052","pdf_url":"https://www.aclweb.org/anthology/P19-2052.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 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8100000023841858,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2949454212.pdf","grobid_xml":"https://content.openalex.org/works/W2949454212.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W38739846","https://openalex.org/W1624488588","https://openalex.org/W2097726431","https://openalex.org/W2139645402","https://openalex.org/W2146277089","https://openalex.org/W2154359981","https://openalex.org/W2251843872","https://openalex.org/W2251939518","https://openalex.org/W2338893019","https://openalex.org/W2470673105","https://openalex.org/W2493916176","https://openalex.org/W2536583325","https://openalex.org/W2584429674","https://openalex.org/W2772253004","https://openalex.org/W2796452002","https://openalex.org/W2949709688","https://openalex.org/W2962920413","https://openalex.org/W2963293280","https://openalex.org/W2963626623","https://openalex.org/W4297813410","https://openalex.org/W4298857826"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2548633793","https://openalex.org/W3013279174","https://openalex.org/W2941935829","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526"],"abstract_inverted_index":{"Code-mixing":[0],"is":[1,18],"the":[2,6,14,47,75,108,112,115],"phenomenon":[3],"of":[4,10,49,52,59,95,111,137],"mixing":[5],"vocabulary":[7],"and":[8,27,101,140,150],"syntax":[9],"multiple":[11],"languages":[12],"in":[13,23,34,122],"same":[15],"sentence.":[16],"It":[17],"an":[19,119],"increasingly":[20],"common":[21],"occurrence":[22],"today's":[24],"multilingual":[25],"society":[26],"poses":[28],"a":[29,43,78,89,133,155],"big":[30],"challenge":[31],"when":[32],"encountered":[33],"different":[35,66,97],"downstream":[36],"tasks.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41],"present":[42],"hybrid":[44],"architecture":[45],"for":[46,74],"task":[48],"Sentiment":[50],"Analysis":[51],"English-Hindi":[53],"code-mixed":[54],"data.":[55],"Our":[56],"method":[57],"consists":[58,94],"three":[60],"components,":[61],"each":[62],"seeking":[63],"to":[64,88,124],"alleviate":[65],"issues.":[67],"We":[68],"first":[69],"generate":[70],"subword":[71],"level":[72],"representations":[73,83],"sentences":[76],"using":[77],"CNN":[79],"architecture.":[80],"The":[81,104],"generated":[82],"are":[84],"used":[85],"as":[86],"inputs":[87],"Dual":[90],"Encoder":[91,106,117],"Network":[92,135],"which":[93],"two":[96],"BiLSTMs":[98],"-the":[99],"Collective":[100,105],"Specific":[102,116],"Encoder.":[103],"captures":[107],"overall":[109],"sentiment":[110],"sentence,":[113],"while":[114],"utilizes":[118],"attention":[120],"mechanism":[121],"order":[123],"focus":[125],"on":[126],"individual":[127],"sentiment-bearing":[128],"sub-words.":[129],"This,":[130],"combined":[131],"with":[132],"Feature":[134],"consisting":[136],"orthographic":[138],"features":[139],"specially":[141],"trained":[142],"word":[143],"embeddings,":[144],"achieves":[145],"state-of-the-art":[146],"results":[147],"-83.54%":[148],"accuracy":[149],"0.827":[151],"F1":[152],"score":[153],"-on":[154],"benchmark":[156],"dataset.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-17T09:09:15.849793","created_date":"2025-10-10T00:00:00"}
