7,036 research outputs found
Multi-dimensional Boltzmann Sampling of Languages
This paper addresses the uniform random generation of words from a
context-free language (over an alphabet of size ), while constraining every
letter to a targeted frequency of occurrence. Our approach consists in a
multidimensional extension of Boltzmann samplers \cite{Duchon2004}. We show
that, under mostly \emph{strong-connectivity} hypotheses, our samplers return a
word of size in and exact frequency in
expected time. Moreover, if we accept tolerance
intervals of width in for the number of occurrences of each
letters, our samplers perform an approximate-size generation of words in
expected time. We illustrate these techniques on the
generation of Tetris tessellations with uniform statistics in the different
types of tetraminoes.Comment: 12p
Unsupervised syntactic chunking with acoustic cues: Computational models for prosodic bootstrapping
Learning to group words into phrases without supervision is a hard task for NLP systems, but infants routinely accomplish it. We hypothesize that infants use acoustic cues to prosody, which NLP systems typically ignore. To evaluate the utility of prosodic information for phrase discovery, we present an HMM-based unsupervised chunker that learns from only transcribed words and raw acoustic correlates to prosody. Unlike previous work on unsupervised parsing and chunking, we use neither gold standard part-of-speech tags nor punctuation in the input. Evaluated on the Switchboard corpus, our model outperforms several baselines that exploit either lexical or prosodic information alone, and, despite producing a flat structure, performs competitively with a state-of-the-art unsupervised lexicalized parser, with a substantial advantage in precision. Our results support the hypothesis that acoustic-prosodic cues provide useful evidence about syntactic phrases for language-learning infants.10 page(s
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