Two decades of statistical language modeling: Where do we go from here

Two decades of statistical language modeling: Where do we go from here (2000)

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by Ronald Rosenfeld

Venue:	Proceedings of the IEEE
Citations:	119 - 1 self

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Datum	Value	Source
TITLE	Two decades of statistical language modeling: Where do we go from here	INFERENCE
AUTHOR NAME	Ronald Rosenfeld	SVM HeaderParse 0.2
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	Pittsburgh, PA 15213; USA	SVM HeaderParse 0.2
ABSTRACT	Statistical Language Models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them here, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data. 1. OUTLINE Statistical language modeling (SLM) is the attempt to capture regularities of natural language for the purpose of improving the performance of various natural language applications. By and large, statistical language modeling amounts to estimating the probability distribution of various linguistic units, such as words, sentences, and whole documents. Statistical language modeling is crucial for a large variety of language technology applications. These include speech recognition (where SLM got its start), machine translation, document classification and routing, optical character recognition, information retrieval, handwriting recognition, spelling correction, and many more. In machine translation, for example, purely statistical approaches have been introduced in [1]. But even researchers using rule-based approaches have found it beneficial to introduce some elements of SLM and statistical estimation [2]. In information retrieval, a language modeling approach was recently proposed by [3], and a statistical/information theoretical approach was developed by [4]. SLM employs statistical estimation techniques using language training data, that is, text. Because of the categorical nature of language, and the large vocabularies people naturally use, statistical techniques must estimate a large number of parameters, and consequently depend critically on the availability of large amounts of training data.	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE	Proceedings of the IEEE	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2000	INFERENCE
CITATIONS	82 found	ParsCit 1.0