{"id":"https://openalex.org/W2888203479","doi":"https://doi.org/10.1145/3233547.3233632","title":"Improving LIWC Using Soft Word Matching","display_name":"Improving LIWC Using Soft Word Matching","publication_year":2018,"publication_date":"2018-08-15","ids":{"openalex":"https://openalex.org/W2888203479","doi":"https://doi.org/10.1145/3233547.3233632","mag":"2888203479"},"language":"en","primary_location":{"id":"doi:10.1145/3233547.3233632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3233547.3233632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","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/A5067285379","display_name":"Yuan Gong","orcid":"https://orcid.org/0000-0002-4537-0078"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Gong","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010805296","display_name":"Kevin Shin","orcid":null},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Shin","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019362963","display_name":"Christian Poellabauer","orcid":"https://orcid.org/0000-0002-0599-7941"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christian Poellabauer","raw_affiliation_strings":["University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"523","last_page":"523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing 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/T10181","display_name":"Natural Language Processing 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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9994000196456909,"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/T12031","display_name":"Speech and dialogue systems","score":0.9922999739646912,"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/natural-language-processing","display_name":"Natural language processing","score":0.6586278676986694},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6551529169082642},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.621204137802124},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6105128526687622},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5461087226867676},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.337935209274292},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.31365132331848145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12552356719970703},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1232849657535553}],"concepts":[{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6586278676986694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551529169082642},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.621204137802124},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6105128526687622},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5461087226867676},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.337935209274292},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.31365132331848145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12552356719970703},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1232849657535553},{"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:10.1145/3233547.3233632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3233547.3233632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8500000238418579,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2081580037","https://openalex.org/W2252180568","https://openalex.org/W2599472880","https://openalex.org/W2915177913","https://openalex.org/W2950577311"],"related_works":["https://openalex.org/W1972035260","https://openalex.org/W1569283511","https://openalex.org/W2794488505","https://openalex.org/W4301594054","https://openalex.org/W3125889879","https://openalex.org/W3124422538","https://openalex.org/W2295467472","https://openalex.org/W3046451053","https://openalex.org/W2097909533","https://openalex.org/W4236193183"],"abstract_inverted_index":{"The":[0,197],"widely":[1],"deployed":[2],"and":[3,7,33,98,188,233],"easy-to-use":[4],"Linguistic":[5],"Inquiry":[6],"Word":[8],"Count":[9],"(LIWC)":[10],"tool":[11],"is":[12,50,106,127,134,180,203],"the":[13,46,118,122,131,175,200,205,214,220],"gold":[14],"standard":[15],"for":[16,22,236],"many":[17,23],"computerized":[18],"text":[19,66,94,133,237],"analysis":[20],"tasks":[21],"medical":[24,47],"applications":[25],"such":[26],"as":[27,84,209,226],"patient":[28],"sentiment":[29],"analysis,":[30],"depression":[31],"detection,":[32],"ADHD":[34],"detection.":[35],"Compared":[36],"to":[37,54,92,117,171,217],"most":[38],"other":[39],"natural":[40],"language":[41,138],"processing":[42],"(NLP)":[43],"tasks,":[44],"in":[45,174,204],"field":[48],"it":[49],"often":[51],"very":[52],"difficult":[53],"obtain":[55],"large-scale":[56],"data":[57],"sets,":[58],"making":[59],"effective":[60,99],"automatic":[61],"representation":[62,238],"learning":[63,90,192,235],"from":[64],"complex":[65],"patterns":[67],"(e.g.,":[68,139],"using":[69,80,182],"a":[70,81,85,88,96,136,151,172,184,190,227],"deep":[71],"auto-encoder)":[72],"challenging.":[73],"LIWC":[74,176],"can":[75,223],"solve":[76],"this":[77,147],"problem":[78,126],"by":[79],"human-designed":[82],"dictionary":[83,105,123],"substitution":[86],"of":[87,121,199,229],"machine":[89,191,234],"model":[91],"convert":[93],"into":[95],"concise":[97],"vector":[100],"representation.":[101],"However,":[102],"while":[103],"LIWC's":[104,210],"large,":[107],"some":[108],"potentially":[109],"informative":[110],"words":[111,167],"might":[112],"still":[113],"be":[114,224],"neglected":[115],"due":[116],"knowledge":[119,232],"constraint":[120],"editors.":[124],"This":[125,178],"particularly":[128],"conspicuous":[129],"when":[130],"analyzed":[132],"not":[135,157],"formal":[137],"dialect,":[140],"slang,":[141],"or":[142],"cyber":[143],"words).":[144],"To":[145],"address":[146],"problem,":[148],"we":[149],"propose":[150],"new":[152],"matching":[153],"scheme":[154,179],"that":[155,168],"does":[156],"require":[158],"an":[159],"exact":[160,206],"word":[161,194],"match,":[162],"but":[163],"instead":[164],"counts":[165],"all":[166],"are":[169],"similar":[170],"key":[173],"dictionary.":[177],"implemented":[181],"WordNet,":[183],"large":[185],"lexical":[186],"database,":[187],"Word2Vec,":[189],"based":[193],"embedding":[195],"technology.":[196],"output":[198],"proposed":[201,221],"method":[202,222],"same":[207],"format":[208],"output,":[211],"thereby":[212],"maintaining":[213],"usability.":[215],"Similar":[216],"previous":[218],"work,":[219],"viewed":[225],"combination":[228],"human":[230],"domain":[231],"encoding.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}