Similarity-based approaches to natural language processing

Similarity-based approaches to natural language processing (1997)

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by Lillian Jane Lee

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Datum	Value	Source
TITLE	Similarity-based approaches to natural language processing	INFERENCE
AUTHOR NAME	Lillian Jane Lee	user correction
AUTHOR AFFIL	Harvard University	user correction
AUTHOR ADDR	Cambridge, Massachusetts	user correction
ABSTRACT	Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and natural-language-based user interfaces. However, even huge bodies of text yield highly unreliable estimates of the probability of relatively common events, and, in fact, perfectly reasonable events may not occur in the training data at all. This is known as the sparse data problem. Traditional approaches to the sparse data problem use crude approximations. We propose a different solution: if we are able to organize the data into classes of similar events, then, if information about an event is lacking, we can estimate its behavior from information about similar events. This thesis presents two such similarity-based approaches, where, in general, we measure similarity by the Kullback-Leibler divergence, an information-theoretic quantity. Our first approach is to build soft, hierarchical clusters: soft, because each event belongs to each cluster with some probability; hierarchical, because cluster centroids are iteratively split to model finer distinctions. Our clustering method, which uses the technique of deterministic annealing,	user correction
YEAR	1997	SVM HeaderParse 0.2
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Ph.D. thesis	INFERENCE
CITATIONS	78 found	ParsCit 1.0