[Colloquium] (no subject)

[Kristen Eisenberg]

[Colloquium] Waxmonsky/Dissertation Defense/Jul 20, 2011


Department of Computer Science/The University of Chicago

*** Dissertation Defense ***


Candidate:  Sonjia Waxmonsky

Date:  Wednesday, July 20, 2011

Time:  10:00 AM

Place:  Ryerson 277

Title: Natural Language Processing for Named Entities with
Word-internal Information

Abstract:
In this thesis we present three projects that focus on natural
language processing of named entities in text data, specifically for
scenarios and domains where rare and out-of-vocabulary (OOV) words are
problematic. First, we present work on the discovery of the language
of origin of a named entity. This has applications in speech and
language processing tasks, such as text-to-speech synthesis, since
language of origin can help predict pronunciation of OOV words. Word
origin recognition has also been studied for demographics and life
sciences as a component in the collection of ethnicity data. Previous
research has applied supervised machine learning methods to automate
this task, but this requires a set of hand-labeled training data for
each language represented in the model. Hand-labeled data may be
expensive to acquire, and additionally the set of origin languages may
not be known a priori. We consider how active learning can be applied
to minimize the amount of manual annotation needed to train a
successful supervised model. We also apply word origin modeling to
grapheme-to-phoneme (G2P) conversion of US surnames, using both
supervised and unsupervised approaches.

Kristen Eisenberg
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