DMCA
Adaptive Statistical Language Modeling: A Maximum Entropy Approach (1994)
Cached
Download Links
| Citations: | 205 - 6 self |
BibTeX
@TECHREPORT{Rosenfeld94adaptivestatistical,
author = {Ronald Rosenfeld and Jaime Carbonell and Alexander Rudnicky},
title = {Adaptive Statistical Language Modeling: A Maximum Entropy Approach},
institution = {},
year = {1994}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Language modeling is the attempt to characterize, capture and exploit regularities in natural language. In statistical language modeling, large amounts of text are used to automatically determine the model's parameters. Language modeling is useful in automatic speech recognition, machine translation, and any other application that processes natural language with incomplete knowledge. In this thesis, I view language as an information source which emits a stream of symbols from a finite alphabet (the vocabulary). The goal of language modeling is then to identify and exploit sources of information in the language stream, so as to minimize its perceived entropy. Most existing statistical language models exploit the immediate past only. To extract information from further back in the document's history, I use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from many sources must...
Keyphrases
maximum entropy approach adaptive statistical language modeling language modeling natural language machine translation exploit regularity language stream statistical evidence trigger pair large amount incomplete knowledge finite alphabet statistical language modeling automatic speech recognition basic information bearing element statistical language model perceived entropy many source information source immediate past
