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A Visualizable Evidence-Driven Approach for Authorship Attribution
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In: https://dl.acm.org/citation.cfm?doid=2744298.2699910 (2015)
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Ten Years of Rich Internet Applications: A Systematic Mapping Study, and Beyond
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On the localness of software
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In: http://macbeth.cs.ucdavis.edu/cache-model.pdf (2014)
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Approximate Semantic Matching of Events for the Internet of Things
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Q.: Scale based region growing for scene text detection
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In: http://www.stat.ucla.edu/%7Ejunhua.mao/papers/Scale_based_region_growing_ACM_MM13.pdf (2013)
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Gigatensor: scaling tensor analysis up by 100 times - algorithms and discoveries
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In: http://www.cs.cmu.edu/~christos/PUBLICATIONS/kdd12-gigatensor.pdf (2012)
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Distributional Semantics with Eyes: Using Image Analysis to Improve Computational Representations of Word Meaning
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In: http://clic.cimec.unitn.it/marco/publications/bruni-etal-acmmm-2012.pdf (2012)
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Integrating document clustering and . . .
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In: http://users.cis.fiu.edu/~taoli/pub/a14-wang.pdf (2011)
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Folks in folksonomies: social link prediction from shared metadata
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In: http://hal.archives-ouvertes.fr/docs/00/42/98/86/PDF/wsdm141-schifanella.pdf (2010)
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Semantic lexicon adaptation for use in query interpretation
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In: http://www.ra.ethz.ch/cdstore/www2010/www/p1167.pdf (2010)
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Classifying latent user attributes in twitter
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In: https://csc-869-mlog.googlecode.com/files/p37-rao.pdf (2010)
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Abstract:
Social media outlets such as Twitter have become an impor-tant forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also in-cludes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and fea-tures provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.
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Keyword:
Algorithms; attribute classifica- tion; Experimentation; General Terms; Human Factors; Languages Keywords; latent attribute; sociolinguistics; Twitter
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.457.9752
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Spatiotemporal mapping of Wikipedia concepts
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In: http://comupedia.org/adrian/articles/jcdl75-popescu.pdf (2010)
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An Information-extraction system for Urdu—a resource-poor language
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In: http://www.cedar.buffalo.edu/~rohini/Papers/ACM-TALIP.pdf (2010)
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Transliteration for resource-scarce languages
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In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010)
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MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis
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In: http://clef.isti.cnr.it/2009/working_notes/morpho-papers/bernhard-paperCLEF2009.pdf (2009)
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OpinionMiner: a novel machine learning system for web opinion mining and extraction
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In: http://www.cedar.buffalo.edu/~rohini/Papers/KDD_Jin.pdf (2009)
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Word sense disambiguation: a survey
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In: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf (2009)
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Sentiment analysis of blogs by combining lexical knowledge with text classification
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In: http://www.prem-melville.com/publications/pooling-multinomials-kdd09.pdf (2009)
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A 2-poisson model for probabilistic coreference of named entities for improved text retrieval
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In: http://www.comp.nus.edu.sg/~nght/pubs/sigir09.pdf (2009)
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