Modelling the Lexicon in Unsupervised Part of Speech Induction

Automatically inducing the syntactic part-of-speech categories for words in text is a fundamental task in Computational Linguistics. While the performance of unsupervised tagging models has been slowly improving, current state-of-the-art systems make the obviously incorrect assumption that all tokens of a given word type must share a single part-of-speech tag. This one-tag-per-type heuristic counters the tendency of Hidden Markov Model based taggers to over generate tags for a given word type. However, it is clearly incompatible with basic syntactic theory. In this paper we extend a state-of-the-art Pitman-Yor Hidden Markov Model tagger with an explicit model of the lexicon. In doing so we are able to incorporate a soft bias towards inducing few tags per type. We develop a particle filter for drawing samples from the posterior of our model and present empirical results that show that our model is competitive with and faster than the state-of-the-art without making any unrealistic restrictions.Comment: To be presented at the 14th Conference of the European Chapter of the Association for Computational Linguistic

Dance evolution : interactively evolving neural networks to control dancing three-dimensional models

The impulse shared by all humans to express ourselves through dance represents a unique opportunity to artificially capture human creative expression. 1hls ambition aligns with the aim of artificial intelligence (AI) to study and emulate those aspects of human intelligence that are not readily reproduced in existing computer algorithms. As a first step toward addressing this challenge, this thesis describes Dance Evolution, which focuses on movements that are tied to a specific beat of music. Furthermore, Dance Evolution harnesses the users own taste to ex pl ore the new and interesting dances, allowing ta novel form of self-expression mediated by the computer, following the trend started by music and rhythm games. By implementing an algorithm that identifies the most prominent sounds within a song, Dance Evolution in effect allows artificial neural networks (ANNs) to listen to any song and exploit its rhythmic structure. Interactive evolution provides a tool for users to search increasingly intricate movement sequences by breeding their ANN controllers, in the same way that a gardener might explore interesting plants by breeding hybrids. The underlying idea in Dance Evolution is thus to create a novel mapping between sound and movement that evokes the spirit of casually dancing to the beat of a song