Bibliography – Natural Language Processing

Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all) Distant Reading | Cultural Analytics | | Sociocultural Approaches | Topic Modeling in DH | Non-consumptive Use


2133649 Natural Language Processing 1 chicago-fullnote-bibliography 50 date desc year 1 1 1 3316 https://we1s.ucsb.edu/wp-content/plugins/zotpress/
%7B%22status%22%3A%22success%22%2C%22updateneeded%22%3Afalse%2C%22instance%22%3Afalse%2C%22meta%22%3A%7B%22request_last%22%3A0%2C%22request_next%22%3A0%2C%22used_cache%22%3Atrue%7D%2C%22data%22%3A%5B%7B%22key%22%3A%22MS4U5EAW%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Rogers%20et%20al.%22%2C%22parsedDate%22%3A%222020%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BRogers%2C%20Anna%2C%20Olga%20Kovaleva%2C%20and%20Anna%20Rumshisky.%20%26%23x201C%3BA%20Primer%20in%20BERTology%3A%20What%20We%20Know%20about%20How%20BERT%20Works.%26%23x201D%3B%20%26lt%3Bi%26gt%3BArXiv%3A2002.12327%20%5BCs%5D%26lt%3B%5C%2Fi%26gt%3B%2C%202020.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%26%23039%3B%26gt%3Bhttp%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%26lt%3B%5C%2Fa%26gt%3B.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3DMS4U5EAW%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22A%20Primer%20in%20BERTology%3A%20What%20we%20know%20about%20how%20BERT%20works%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anna%22%2C%22lastName%22%3A%22Rogers%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Olga%22%2C%22lastName%22%3A%22Kovaleva%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Anna%22%2C%22lastName%22%3A%22Rumshisky%22%7D%5D%2C%22abstractNote%22%3A%22Transformer-based%20models%20are%20now%20widely%20used%20in%20NLP%2C%20but%20we%20still%20do%20not%20understand%20a%20lot%20about%20their%20inner%20workings.%20This%20paper%20describes%20what%20is%20known%20to%20date%20about%20the%20famous%20BERT%20model%20%28Devlin%20et%20al.%202019%29%2C%20synthesizing%20over%2040%20analysis%20studies.%20We%20also%20provide%20an%20overview%20of%20the%20proposed%20modifications%20to%20the%20model%20and%20its%20training%20regime.%20We%20then%20outline%20the%20directions%20for%20further%20research.%22%2C%22date%22%3A%222020%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISSN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Farxiv.org%5C%2Fabs%5C%2F2002.12327%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-02-29T06%3A39%3A16Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Artificial%20intelligence%22%7D%2C%7B%22tag%22%3A%22Interpretability%20and%20explainability%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Text%20Analysis%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22RKCERR67%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ahmed%20et%20al.%22%2C%22parsedDate%22%3A%222017%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BAhmed%2C%20Hadeer%2C%20Issa%20Traore%2C%20and%20Sherif%20Saad.%20%26%23x201C%3BDetection%20of%20Online%20Fake%20News%20Using%20N-Gram%20Analysis%20and%20Machine%20Learning%20Techniques.%26%23x201D%3B%20In%20%26lt%3Bi%26gt%3BIntelligent%2C%20Secure%2C%20and%20Dependable%20Systems%20in%20Distributed%20and%20Cloud%20Environments%26lt%3B%5C%2Fi%26gt%3B%2C%20edited%20by%20Issa%20Traore%2C%20Isaac%20Woungang%2C%20and%20Ahmed%20Awad%2C%20127%26%23x2013%3B38.%20Lecture%20Notes%20in%20Computer%20Science.%20Cham%3A%20Springer%20International%20Publishing%2C%202017.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1007%5C%2F978-3-319-69155-8_9%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1007%5C%2F978-3-319-69155-8_9%26lt%3B%5C%2Fa%26gt%3B.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3DRKCERR67%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Detection%20of%20Online%20Fake%20News%20Using%20N-Gram%20Analysis%20and%20Machine%20Learning%20Techniques%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Hadeer%22%2C%22lastName%22%3A%22Ahmed%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Issa%22%2C%22lastName%22%3A%22Traore%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Sherif%22%2C%22lastName%22%3A%22Saad%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Issa%22%2C%22lastName%22%3A%22Traore%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Isaac%22%2C%22lastName%22%3A%22Woungang%22%7D%2C%7B%22creatorType%22%3A%22editor%22%2C%22firstName%22%3A%22Ahmed%22%2C%22lastName%22%3A%22Awad%22%7D%5D%2C%22abstractNote%22%3A%22Fake%20news%20is%20a%20phenomenon%20which%20is%20having%20a%20significant%20impact%20on%20our%20social%20life%2C%20in%20particular%20in%20the%20political%20world.%20Fake%20news%20detection%20is%20an%20emerging%20research%20area%20which%20is%20gaining%20interest%20but%20involved%20some%20challenges%20due%20to%20the%20limited%20amount%20of%20resources%20%28i.e.%2C%20datasets%2C%20published%20literature%29%20available.%20We%20propose%20in%20this%20paper%2C%20a%20fake%20news%20detection%20model%20that%20use%20n-gram%20analysis%20and%20machine%20learning%20techniques.%20We%20investigate%20and%20compare%20two%20different%20features%20extraction%20techniques%20and%20six%20different%20machine%20classification%20techniques.%20Experimental%20evaluation%20yields%20the%20best%20performance%20using%20Term%20Frequency-Inverted%20Document%20Frequency%20%28TF-IDF%29%20as%20feature%20extraction%20technique%2C%20and%20Linear%20Support%20Vector%20Machine%20%28LSVM%29%20as%20a%20classifier%2C%20with%20an%20accuracy%20of%2092%25.%22%2C%22date%22%3A%222017%22%2C%22proceedingsTitle%22%3A%22Intelligent%2C%20Secure%2C%20and%20Dependable%20Systems%20in%20Distributed%20and%20Cloud%20Environments%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1007%5C%2F978-3-319-69155-8_9%22%2C%22ISBN%22%3A%22978-3-319-69155-8%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T08%3A09%3A54Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20mining%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22FGCXV2ID%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Conroy%20et%20al.%22%2C%22parsedDate%22%3A%222015%22%2C%22numChildren%22%3A1%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BConroy%2C%20Niall%20J.%2C%20Victoria%20L.%20Rubin%2C%20and%20Yimin%20Chen.%20%26%23x201C%3BAutomatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News%3A%20Automatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News.%26%23x201D%3B%20%26lt%3Bi%26gt%3BProceedings%20of%20the%20Association%20for%20Information%20Science%20and%20Technology%26lt%3B%5C%2Fi%26gt%3B%2052%2C%20no.%201%20%282015%29%3A%201%26%23x2013%3B4.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-DOIURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fpra2.2015.145052010082%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Fdoi.org%5C%2F10.1002%5C%2Fpra2.2015.145052010082%26lt%3B%5C%2Fa%26gt%3B.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3DFGCXV2ID%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22journalArticle%22%2C%22title%22%3A%22Automatic%20deception%20detection%3A%20Methods%20for%20finding%20fake%20news%3A%20Automatic%20Deception%20Detection%3A%20Methods%20for%20Finding%20Fake%20News%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Niall%20J.%22%2C%22lastName%22%3A%22Conroy%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Victoria%20L.%22%2C%22lastName%22%3A%22Rubin%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Yimin%22%2C%22lastName%22%3A%22Chen%22%7D%5D%2C%22abstractNote%22%3A%22This%20research%20surveys%20the%20current%20state%5Cu2010of%5Cu2010the%5Cu2010art%20technologies%20that%20are%20instrumental%20in%20the%20adoption%20and%20development%20of%20fake%20news%20detection.%20%5Cu201cFake%20news%20detection%5Cu201d%20is%20defined%20as%20the%20task%20of%20categorizing%20news%20along%20a%20continuum%20of%20veracity%2C%20with%20an%20associated%20measure%20of%20certainty.%20Veracity%20is%20compromised%20by%20the%20occurrence%20of%20intentional%20deceptions.%20The%20nature%20of%20online%20news%20publication%20has%20changed%2C%20such%20that%20traditional%20fact%20checking%20and%20vetting%20from%20potential%20deception%20is%20impossible%20against%20the%20flood%20arising%20from%20content%20generators%2C%20as%20well%20as%20various%20formats%20and%20genres.%5Cn%5CnThe%20paper%20provides%20a%20typology%20of%20several%20varieties%20of%20veracity%20assessment%20methods%20emerging%20from%20two%20major%20categories%20%5Cu2013%20linguistic%20cue%20approaches%20%28with%20machine%20learning%29%2C%20and%20network%20analysis%20approaches.%20We%20see%20promise%20in%20an%20innovative%20hybrid%20approach%20that%20combines%20linguistic%20cue%20and%20machine%20learning%2C%20with%20network%5Cu2010based%20behavioral%20data.%20Although%20designing%20a%20fake%20news%20detector%20is%20not%20a%20straightforward%20problem%2C%20we%20propose%20operational%20guidelines%20for%20a%20feasible%20fake%20news%20detecting%20system.%22%2C%22date%22%3A%222015%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%2210.1002%5C%2Fpra2.2015.145052010082%22%2C%22ISSN%22%3A%2223739231%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fdoi.wiley.com%5C%2F10.1002%5C%2Fpra2.2015.145052010082%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-04-01T07%3A05%3A13Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Data%20science%22%7D%2C%7B%22tag%22%3A%22Fake%20news%22%7D%2C%7B%22tag%22%3A%22Journalism%22%7D%2C%7B%22tag%22%3A%22Machine%20learning%22%7D%2C%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Network%20analysis%22%7D%5D%7D%7D%2C%7B%22key%22%3A%227RCXUJRT%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Mimno%20and%20Blei%22%2C%22parsedDate%22%3A%222011%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BMimno%2C%20David%2C%20and%20David%20Blei.%20%26%23x201C%3BBayesian%20Checking%20for%20Topic%20Models.%26%23x201D%3B%20In%20%26lt%3Bi%26gt%3BProceedings%20of%20the%20Conference%20on%20Empirical%20Methods%20in%20Natural%20Language%20Processing%26lt%3B%5C%2Fi%26gt%3B%2C%20227%26%23x2013%3B37.%20Association%20for%20Computational%20Linguistics%2C%202011.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3D7RCXUJRT%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Bayesian%20checking%20for%20topic%20models%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%22%2C%22lastName%22%3A%22Mimno%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22David%22%2C%22lastName%22%3A%22Blei%22%7D%5D%2C%22abstractNote%22%3A%22Real%20document%20collections%20do%20not%20fit%20the%20independence%20assumptions%20asserted%20by%20most%20statistical%20topic%20models%2C%20but%20how%20badly%20do%20they%20violate%20them%3F%20This%20paper%20presents%20a%20Bayesian%20method%20for%20measuring%20how%20well%20a%20topic%20model%20fits%20a%20corpus.%20The%20approach%20is%20based%20on%20posterior%20predictive%20checking%2C%20a%20method%20for%20diagnosing%20Bayesian%20models%20in%20user-defined%20ways.%20The%20method%20can%20identify%20where%20a%20topic%20model%20fits%20the%20data%2C%20where%20it%20falls%20short%2C%20and%20in%20which%20directions%20it%20might%20be%20improved.%22%2C%22date%22%3A%222011%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%20conference%20on%20empirical%20methods%20in%20natural%20language%20processing%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222020-07-01T20%3A57%3A38Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20optimization%22%7D%2C%7B%22tag%22%3A%22Topic%20modeling%22%7D%5D%7D%7D%2C%7B%22key%22%3A%225RKUY2I6%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22creatorSummary%22%3A%22Ratinov%20et%20al.%22%2C%22parsedDate%22%3A%222011%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BRatinov%2C%20Lev%2C%20Dan%20Roth%2C%20Doug%20Downey%2C%20and%20Mike%20Anderson.%20%26%23x201C%3BLocal%20and%20Global%20Algorithms%20for%20Disambiguation%20to%20Wikipedia%2C%26%23x201D%3B%201375%26%23x2013%3B84%2C%202011.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttps%3A%5C%2F%5C%2Faclweb.org%5C%2Fanthology%5C%2Fpapers%5C%2FP%5C%2FP11%5C%2FP11-1138%5C%2F%26%23039%3B%26gt%3Bhttps%3A%5C%2F%5C%2Faclweb.org%5C%2Fanthology%5C%2Fpapers%5C%2FP%5C%2FP11%5C%2FP11-1138%5C%2F%26lt%3B%5C%2Fa%26gt%3B.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3D5RKUY2I6%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Local%20and%20Global%20Algorithms%20for%20Disambiguation%20to%20Wikipedia%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Lev%22%2C%22lastName%22%3A%22Ratinov%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Dan%22%2C%22lastName%22%3A%22Roth%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Doug%22%2C%22lastName%22%3A%22Downey%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Mike%22%2C%22lastName%22%3A%22Anderson%22%7D%5D%2C%22abstractNote%22%3A%22Disambiguating%20concepts%20and%20entities%20in%20a%20context%20sensitive%20way%20is%20a%20fundamental%20problem%20in%20natural%20language%20processing.%20The%20comprehensiveness%20of%20Wikipedia%20has%20made%20the%20online%20encyclopedia%20an%20increasingly%20popular%20target%20for%20disambiguation.%20Disambiguation%20to%20Wikipedia%20is%20similar%20to%20a%20traditional%20Word%20Sense%20Disambiguation%20task%2C%20but%20distinct%20in%20that%20the%20Wikipedia%20link%20structure%20provides%20additional%20information%20about%20which%20disambiguations%20are%20compatible.%20In%20this%20work%20the%20authors%20analyze%20approaches%20that%20utilize%20this%20information%20to%20arrive%20at%20coherent%20sets%20of%20disambiguations%20for%20a%20given%20document%20%28which%20we%20call%20%5Cu201cglobal%5Cu201d%20approaches%29%2C%20and%20compare%20them%20to%20more%20traditional%20%28local%29%20approaches.%20They%20show%20that%20previous%20approaches%20for%20global%20disambiguation%20can%20be%20improved%2C%20but%20even%20then%20the%20local%20disambiguation%20provides%20a%20baseline%20which%20is%20very%20hard%20to%20beat.%22%2C%22date%22%3A%222011%22%2C%22proceedingsTitle%22%3A%22%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22https%3A%5C%2F%5C%2Faclweb.org%5C%2Fanthology%5C%2Fpapers%5C%2FP%5C%2FP11%5C%2FP11-1138%5C%2F%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A43%3A25Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20optimization%22%7D%2C%7B%22tag%22%3A%22Wikification%22%7D%5D%7D%7D%2C%7B%22key%22%3A%22H6QJSRSF%22%2C%22library%22%3A%7B%22id%22%3A2133649%7D%2C%22meta%22%3A%7B%22lastModifiedByUser%22%3A%7B%22id%22%3A22837%2C%22username%22%3A%22ayliu%22%2C%22name%22%3A%22Alan%20Liu%22%2C%22links%22%3A%7B%22alternate%22%3A%7B%22href%22%3A%22https%3A%5C%2F%5C%2Fwww.zotero.org%5C%2Fayliu%22%2C%22type%22%3A%22text%5C%2Fhtml%22%7D%7D%7D%2C%22creatorSummary%22%3A%22Boyd-Graber%20and%20Resnik%22%2C%22parsedDate%22%3A%222010%22%2C%22numChildren%22%3A0%7D%2C%22bib%22%3A%22%26lt%3Bdiv%20class%3D%26quot%3Bcsl-bib-body%26quot%3B%20style%3D%26quot%3Bline-height%3A%201.35%3B%20padding-left%3A%201em%3B%20text-indent%3A-1em%3B%26quot%3B%26gt%3B%5Cn%20%20%26lt%3Bdiv%20class%3D%26quot%3Bcsl-entry%26quot%3B%26gt%3BBoyd-Graber%2C%20Jordan%2C%20and%20Philip%20Resnik.%20%26%23x201C%3BHolistic%20Sentiment%20Analysis%20across%20Languages%3A%20Multilingual%20Supervised%20Latent%20Dirichlet%20Allocation.%26%23x201D%3B%20In%20%26lt%3Bi%26gt%3BProceedings%20of%20the%202010%20Conference%20on%20Empirical%20Methods%20in%20Natural%20Language%20Processing%26lt%3B%5C%2Fi%26gt%3B%2C%2045%26%23x2013%3B55.%20Association%20for%20Computational%20Linguistics%2C%202010.%20%26lt%3Ba%20class%3D%26%23039%3Bzp-ItemURL%26%23039%3B%20href%3D%26%23039%3Bhttp%3A%5C%2F%5C%2Fwww.aclweb.org%5C%2Fanthology%5C%2FD10-1005%26%23039%3B%26gt%3Bhttp%3A%5C%2F%5C%2Fwww.aclweb.org%5C%2Fanthology%5C%2FD10-1005%26lt%3B%5C%2Fa%26gt%3B.%20%26lt%3Ba%20title%3D%26%23039%3BCite%20in%20RIS%20Format%26%23039%3B%20class%3D%26%23039%3Bzp-CiteRIS%26%23039%3B%20data-zp-cite%3D%26%23039%3Bapi_user_id%3D2133649%26amp%3Bitem_key%3DH6QJSRSF%26%23039%3B%20href%3D%26%23039%3Bjavascript%3Avoid%280%29%3B%26%23039%3B%26gt%3BCite%26lt%3B%5C%2Fa%26gt%3B%20%26lt%3B%5C%2Fdiv%26gt%3B%5Cn%26lt%3B%5C%2Fdiv%26gt%3B%22%2C%22data%22%3A%7B%22itemType%22%3A%22conferencePaper%22%2C%22title%22%3A%22Holistic%20sentiment%20analysis%20across%20languages%3A%20Multilingual%20supervised%20latent%20Dirichlet%20allocation%22%2C%22creators%22%3A%5B%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Jordan%22%2C%22lastName%22%3A%22Boyd-Graber%22%7D%2C%7B%22creatorType%22%3A%22author%22%2C%22firstName%22%3A%22Philip%22%2C%22lastName%22%3A%22Resnik%22%7D%5D%2C%22abstractNote%22%3A%22This%20paper%20develops%20multilingual%20supervised%20latent%20Dirichlet%20allocation%20%28MlSLDA%29%2C%20a%20probabilistic%20generative%20model%20that%20allows%20insights%20gleaned%20from%20one%20language%26%23039%3Bs%20data%20to%20inform%20how%20the%20model%20captures%20properties%20of%20other%20languages.%20This%20work%20shows%20MlSLDA%20can%20build%20topics%20that%20are%20consistent%20across%20languages%2C%20discover%20sensible%20bilingual%20lexical%20correspondences%2C%20and%20leverage%20multilingual%20corpora%20to%20better%20predict%20sentiment.%22%2C%22date%22%3A%222010%22%2C%22proceedingsTitle%22%3A%22Proceedings%20of%20the%202010%20Conference%20on%20Empirical%20Methods%20in%20Natural%20Language%20Processing%22%2C%22conferenceName%22%3A%22%22%2C%22language%22%3A%22en%22%2C%22DOI%22%3A%22%22%2C%22ISBN%22%3A%22%22%2C%22url%22%3A%22http%3A%5C%2F%5C%2Fwww.aclweb.org%5C%2Fanthology%5C%2FD10-1005%22%2C%22collections%22%3A%5B%5D%2C%22dateModified%22%3A%222019-07-27T21%3A33%3A38Z%22%2C%22tags%22%3A%5B%7B%22tag%22%3A%22Natural%20language%20processing%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20algorithm%22%7D%2C%7B%22tag%22%3A%22Topic%20model%20multilingual%22%7D%2C%7B%22tag%22%3A%22Topic%20modeling%22%7D%5D%7D%7D%5D%7D
Rogers, Anna, Olga Kovaleva, and Anna Rumshisky. “A Primer in BERTology: What We Know about How BERT Works.” ArXiv:2002.12327 [Cs], 2020. http://arxiv.org/abs/2002.12327. Cite
Ahmed, Hadeer, Issa Traore, and Sherif Saad. “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques.” In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, edited by Issa Traore, Isaac Woungang, and Ahmed Awad, 127–38. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-69155-8_9. Cite
Conroy, Niall J., Victoria L. Rubin, and Yimin Chen. “Automatic Deception Detection: Methods for Finding Fake News: Automatic Deception Detection: Methods for Finding Fake News.” Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1–4. https://doi.org/10.1002/pra2.2015.145052010082. Cite
Mimno, David, and David Blei. “Bayesian Checking for Topic Models.” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 227–37. Association for Computational Linguistics, 2011. Cite
Ratinov, Lev, Dan Roth, Doug Downey, and Mike Anderson. “Local and Global Algorithms for Disambiguation to Wikipedia,” 1375–84, 2011. https://aclweb.org/anthology/papers/P/P11/P11-1138/. Cite
Boyd-Graber, Jordan, and Philip Resnik. “Holistic Sentiment Analysis across Languages: Multilingual Supervised Latent Dirichlet Allocation.” In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 45–55. Association for Computational Linguistics, 2010. http://www.aclweb.org/anthology/D10-1005. Cite