{"id":"https://openalex.org/W1983123166","doi":"https://doi.org/10.1109/ctc.2014.9","title":"A Comparative Study of Likelihood Ratio Based Forensic Text Comparison Procedures: Multivariate Kernel Density with Lexical Features vs. Word N-grams vs. Character N-grams","display_name":"A Comparative Study of Likelihood Ratio Based Forensic Text Comparison Procedures: Multivariate Kernel Density with Lexical Features vs. Word N-grams vs. Character N-grams","publication_year":2014,"publication_date":"2014-11-01","ids":{"openalex":"https://openalex.org/W1983123166","doi":"https://doi.org/10.1109/ctc.2014.9","mag":"1983123166"},"language":"en","primary_location":{"id":"doi:10.1109/ctc.2014.9","doi":"https://doi.org/10.1109/ctc.2014.9","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ctc.2014.9","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 Fifth Cybercrime and Trustworthy Computing Conference","raw_type":"proceedings-article"},"volume":null,"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/A5005415676","display_name":"Shunichi Ishihara","orcid":"https://orcid.org/0000-0001-6633-3316","observed_orcids":["https://orcid.org/0000-0001-6633-3316"]},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Shunichi Ishihara","raw_affiliation_strings":["Australian National University, Canberra, ACT, AU","Dept. of Linguistics, Australian Nat. Univ., Canberra, ACT, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Australian National University, Canberra, ACT, AU","institution_ids":["https://openalex.org/I118347636"]},{"raw_affiliation_string":"Dept. of Linguistics, Australian Nat. Univ., Canberra, ACT, Australia","institution_ids":["https://openalex.org/I118347636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5005415676"],"corresponding_institution_ids":["https://openalex.org/I118347636"],"apc_list":null,"apc_paid":null,"fwci":0.1963,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.40221716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","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/T12380","display_name":"Authorship Attribution and Profiling","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":0.0,"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/T11800","display_name":"User Authentication and Security Systems","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/authorship-attribution","display_name":"authorship attribution","score":0.9390000104904175},{"id":"https://openalex.org/keywords/likelihood-ratios","display_name":"likelihood ratios","score":0.9259999990463257},{"id":"https://openalex.org/keywords/lexical-features","display_name":"lexical features","score":0.9710000157356262},{"id":"https://openalex.org/keywords/word-n-grams","display_name":"word n-grams","score":0.9679999947547913},{"id":"https://openalex.org/keywords/character-n-grams","display_name":"character n-grams","score":1.0},{"id":"https://openalex.org/keywords/credible-intervals","display_name":"credible intervals","score":0.7829999923706055},{"id":"https://openalex.org/keywords/sample-size-effects","display_name":"sample size effects","score":0.6169999837875366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6032497882843018},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5955453515052795},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5830820798873901},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5620755553245544},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5333806276321411},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.5065823793411255},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5050247311592102},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.452913761138916},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.449628621339798},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.44331350922584534},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4147493243217468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26800885796546936},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25239378213882446},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ctc.2014.9","doi":"https://doi.org/10.1109/ctc.2014.9","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ctc.2014.9","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 Fifth Cybercrime and Trustworthy Computing Conference","raw_type":"proceedings-article"},{"id":"pmh:oai:openresearch-repository.anu.edu.au:1885/102627","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/1885/102627","pdf_url":null,"source":{"id":"https://openalex.org/S4306402539","display_name":"ANU Open Research (Australian National University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I118347636","host_organization_name":"Australian National University","host_organization_lineage":["https://openalex.org/I118347636"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proceedings - 5th Cybercrime and Trustworthy Computing Conference, CTC 2014","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W205402702","https://openalex.org/W250991979","https://openalex.org/W562126715","https://openalex.org/W580854609","https://openalex.org/W1579838312","https://openalex.org/W1590245119","https://openalex.org/W1816167855","https://openalex.org/W1968952713","https://openalex.org/W1975169440","https://openalex.org/W1975363639","https://openalex.org/W1975653240","https://openalex.org/W1986391229","https://openalex.org/W1997621936","https://openalex.org/W2009361097","https://openalex.org/W2023238506","https://openalex.org/W2035535009","https://openalex.org/W2038980261","https://openalex.org/W2041823554","https://openalex.org/W2043976122","https://openalex.org/W2048524363","https://openalex.org/W2066917933","https://openalex.org/W2077831000","https://openalex.org/W2087046691","https://openalex.org/W2099473511","https://openalex.org/W2112044607","https://openalex.org/W2121691771","https://openalex.org/W2125962012","https://openalex.org/W2149712981","https://openalex.org/W2156572966","https://openalex.org/W2165214536","https://openalex.org/W2179658543","https://openalex.org/W2212044511","https://openalex.org/W2214867628","https://openalex.org/W2220449016","https://openalex.org/W2397921183","https://openalex.org/W2787749532","https://openalex.org/W2955901475","https://openalex.org/W3151491250","https://openalex.org/W4242156652","https://openalex.org/W4300417482","https://openalex.org/W4301137208","https://openalex.org/W6712648689"],"related_works":["https://openalex.org/W2733135843","https://openalex.org/W2990458953","https://openalex.org/W2563484817","https://openalex.org/W3163587204","https://openalex.org/W2910775943","https://openalex.org/W580854609","https://openalex.org/W2799460969","https://openalex.org/W1992493611","https://openalex.org/W2165214536","https://openalex.org/W3134210594"],"abstract_inverted_index":{"This":[0,86,121,198],"is":[1,142,206],"a":[2,28,40,48,69,135,140,203,221],"comparative":[3],"study":[4,87,179,199],"to":[5,23,74,96,107,124,146],"empirically":[6],"investigate":[7,75],"the":[8,55,76,79,82,109,126,132,156,168,182,186,191,210,214],"performances":[9],"of":[10,58,78,103,112,128,134,139,171,190,195],"three":[11,60,173,192,215],"different":[12,169],"procedures":[13,22,61,174,216],"for":[14,99,208],"calculating":[15],"authorship":[16,110],"attribution":[17,111],"likelihood":[18],"ratios":[19],"(LR).":[20],"The":[21,101,137],"be":[24],"compared":[25],"are:":[26],"1)":[27],"procedure":[29,41,49,184,188],"based":[30,42,50],"on":[31,43,51,131],"multivariate":[32],"kernel":[33],"density":[34],"(MVKD)":[35],"with":[36,144],"lexical":[37],"features;":[38],"2)":[39],"word":[44,104],"N-grams;":[45],"and":[46,118,151,160],"3)":[47],"character":[52],"N-grams.":[53],"Furthermore,":[54],"best-performing":[56,187],"LRs":[57,67,211],"these":[59,172],"are":[62,116],"fused":[63],"into":[64],"combined":[65],"single":[66],"using":[68,155],"logistic-regression":[70,204],"fusion,":[71],"in":[72,175,193,217,220,224],"order":[73],"extent":[77],"improvement/deterioration":[80],"that":[81,181,202],"fusion":[83,205],"brings":[84],"about.":[85],"uses":[88],"chatlog":[89],"messages,":[90],"which":[91],"were":[92],"presented":[93],"as":[94],"evidence":[95],"prosecute":[97],"paedophiles,":[98],"testing.":[100],"numbers":[102],"tokens":[105],"used":[106],"model":[108],"each":[113],"message":[114],"group":[115],"500":[117],"1000":[119],"words.":[120],"was":[122,185],"done":[123],"examine":[125],"effect":[127],"sample":[129],"size":[130],"performance":[133,138],"system.":[136],"system":[141],"assessed":[143],"regard":[145],"its":[147],"validity":[148],"(=":[149,153],"accuracy)":[150],"reliability":[152],"precision)":[154],"log-likelihood-ratio":[157],"cost":[158],"(Cllr)":[159],"95%":[161],"credible":[162],"intervals":[163],"(CI),":[164],"respectively.":[165],"While":[166],"describing":[167],"characteristics":[170],"their":[176],"outcomes,":[177],"this":[178],"demonstrates":[180,201],"MVKD":[183],"out":[189],"terms":[194],"Cllr":[196],".":[197],"also":[200],"useful":[207],"combining":[209],"obtained":[212],"from":[213],"question,":[218],"resulting":[219],"good":[222],"improvement":[223],"performance.":[225]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}