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Voice Quality

By John H. Esling, Scott R. Moisik, Allison Benner, Lise Crevier-Buchman

Voice Quality "The first description of voice quality production in forty years, this book provides a new framework for its study: The Laryngeal Articulator Model. Informed by instrumental examinations of the laryngeal articulatory mechanism, it revises our understanding of articulatory postures to explain the actions, vibrations and resonances generated in the epilarynx and pharynx."

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Let's Talk

By David Crystal

Let's Talk "Explores the factors that motivate so many different kinds of talk and reveals the rules we use unconsciously, even in the most routine exchanges of everyday conversation."

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Dissertation Information

Title: Bootstrapping Structure into Language: Alignment-based learning Add Dissertation
Author: Menno van Zaanen Update Dissertation
Email: click here to access email
Institution: University of Leeds, School of Computing
Completed in: 2002
Linguistic Subfield(s): Computational Linguistics;
Director(s): Rens Bod
Eric Atwell

Abstract: This thesis introduces a new unsupervised learning framework, called Alignment-Based Learning, which is based on the alignment of sentences and Harris's (1951) notion of substitutability. Instances of the framework can be applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled, bracketed version of that corpus.

Firstly, the framework aligns all sentences in the corpus in pairs, resulting in a partition of the sentences consisting of parts of the sentences that are equal in both sentences and parts that are unequal. Unequal parts of sentences can be seen as being substitutable for each other, since substituting one unequal part for the other results in another valid sentence. The unequal parts of the sentences are thus considered to be possible (possibly overlapping) constituents, called hypotheses.

Secondly, the selection learning phase considers all hypotheses found by the alignment learning phase and selects the best of these. The hypotheses are selected based on the order in which they were found, or based on a probabilistic function.

The framework can be extended with a grammar extraction phase. This extended framework is called parseABL. Instead of returning a structured version of the unstructured input corpus, like the ABL system, this system also returns a stochastic context-free or tree substitution grammar.

Different instances of the framework have been tested on the English ATIS corpus, the Dutch OVIS corpus and the Wall Street Journal corpus. One of the interesting results, apart from the encouraging numerical results, is that all instances can (and do) learn recursive structures.