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Adaptive text mining: Inferring structure from sequences
Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies the way in which the adaptive techniques used in text compression can be applied to text mining. It develops several examples: extraction of hierarchical phrase structures from text, identification of keyphrases in documents, locating proper names and quantities of interest in a piece of text, text categorization, word segmentation, acronym extraction, and structure recognition. We conclude that compression forms a sound unifying principle that allows many text mining problems to be tacked adaptively
The Unsupervised Acquisition of a Lexicon from Continuous Speech
We present an unsupervised learning algorithm that acquires a
natural-language lexicon from raw speech. The algorithm is based on the optimal
encoding of symbol sequences in an MDL framework, and uses a hierarchical
representation of language that overcomes many of the problems that have
stymied previous grammar-induction procedures. The forward mapping from symbol
sequences to the speech stream is modeled using features based on articulatory
gestures. We present results on the acquisition of lexicons and language models
from raw speech, text, and phonetic transcripts, and demonstrate that our
algorithm compares very favorably to other reported results with respect to
segmentation performance and statistical efficiency.Comment: 27 page technical repor
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