Unsupervised Linguistic Inference Lamar, Michael Taylor creator 2010 xiii, 152 p. born digital Thesis (Ph.D. -- Brown University (2010) Bienenstock, Lucien Director Geman, Stuart Reader Charniak, Eugene Reader Brown University. Applied Mathematics sponsor theses This dissertation describes two novel algorithms for fully unsupervised part-of-speech tagging (POST), both of which exceed the performance of the current state-of-the-art models in both accuracy and computational cost. The first iterative algorithm produces a collection of geometric descriptor vectors that characterize each word type along with a clustering of these vectors which provide the inferred POS labels. The second algorithm also produces a clustering of descriptor vectors, but it relies on simple biologically-plausible mechanisms which cause these vectors to self-organize in response to the presentation of natural language data. These biological mechanisms are extended for use in a related algorithm for grammar induction which is shown to perform well on simple context free grammars. Finally, a careful examination of the three most common evaluation criteria for unsupervised POST is included which demonstrates that only one of these three criteria performs satisfactorily. part of speech tagging unsupervised Computational linguistics RPB 20111003 engEnglish10.7301/Z07M0655In CopyrightCollection is open for research.dissertations