http://www.bibsonomy.org/burst/user/renew/nlpBibSonomy BuRST Feed for /user/renew/nlp2008-07-21T01:31:08+02:00Welcome to IEEE Xplore 2.0: Special Section on Natural Language in Software Engineeringhttp://www.bibsonomy.org/bibtex/2d55ef372846dae639344c4932e252ff2/renewrenew2008-03-22T18:45:00+01:00nlp engineering software <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Sawyer">Sawyer</a> and V. <a href="http://www.bibsonomy.org/author/Gervasi">Gervasi</a> </span>(<em>2008</em>)Sat Mar 22 18:45:00 CET 2008Software, IET1-2Special Section on Natural Language in Software Engineering22008nlp engineering software Not Availablestuff from citeyoulikehttp://www.bibsonomy.org/bibtex/22d8f740fe023824a89405eaaddc4bfce/renewrenew2008-02-27T00:48:49+01:00wikipedia lexical semantics nlp <span style="color:#555555;">Torsten <a href="http://www.bibsonomy.org/author/Zesch">Zesch</a> and Iryna <a href="http://www.bibsonomy.org/author/Gurevych">Gurevych</a> and Max <a href="http://www.bibsonomy.org/author/Mühlhäuser">M&#252;hlh&#228;user</a> </span><em>Biannual Conference of the Society for Computational Linguistics and Language Technology, </em>(<em>2007</em>)Wed Feb 27 00:48:49 CET 2008Biannual Conference of the Society for Computational Linguistics and Language TechnologyAnalyzing and Accessing Wikipedia as a Lexical Semantic Resource2007wikipedia lexical semantics nlp We analyze Wikipedia as a lexical semantic resource and compare it with conventional resources, such as dictionaries, thesauri, semantic wordnets, etc. Different parts of Wikipedia reflect different aspects of these resources. We show that Wikipedia contains a vast amount of knowledge about, e.g., named entities, domain specific terms, and rare word senses. If Wikipedia is to be used as a lexical semantic resource in large-scale NLP tasks, efficient programmatic access to the knowledge therein is required. We review existing access mechanisms and show that they are limited with respect to performance and the provided access functions. Therefore, we introduce a general purpose, high performance Java-based Wikipedia API that overcomes these limitations. It is available for research purposes at http://www.ukp.tu-darmstadt.de/software/WikipediaAPI.You are what you sayhttp://www.bibsonomy.org/bibtex/297d07bfe67a320c6f764ca4b361985b3/renewrenew2008-02-22T21:03:38+01:00nlp privacy <span style="color:#555555;">Dan <a href="http://www.bibsonomy.org/author/Frankowski">Frankowski</a> and Dan <a href="http://www.bibsonomy.org/author/Cosley">Cosley</a> and Shilad <a href="http://www.bibsonomy.org/author/Sen">Sen</a> and Loren <a href="http://www.bibsonomy.org/author/Terveen">Terveen</a> and John <a href="http://www.bibsonomy.org/author/Riedl">Riedl</a> </span><em>SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, </em><em>page565--572. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Fri Feb 22 21:03:38 CET 2008New York, NY, USASIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval565--572You are what you say: privacy risks of public mentions2006nlp privacy In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.