This paper presents a set of mathematical and computational tools for manipulating and reasoning about regular languages and regular relations and argues that they provide a solid basis for computational phonology. It shows in detail how this framework applies to ordered sets of context-sensitive rewriting rules and also to grammars in Koskenniemi's two-level formalism. This analysis provides a common representation of phonological constraints that supports efficient generation and recognition by a single simple interpreter. 1.

We describe a generarive probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.

We present a general framework based on weighted finite automata and weighted finite-state transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including context-dependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.

Abstract not found

Several recent stochastic parsers use bilexical grammars, where each word type idiosyncratically prefers particular complements with particular head words. We present O(n^4) parsing algorithms for two bilexical formalisms, improving the prior upper bounds of O(n^5). For a common special case that was known to allow O(n³) parsing (Eisner, 1997), we present an O(n³) algorithm with an improved grammar constant.

this paper, we study the problem of lexicalizing context-free grammars and show that it enables faster processing. In previous attempts to take advantage of lexicalization, a variety of lexicalization procedures have been developed that convert context-free grammars (CFGs) into equivalent lexicalized grammars. However, these procedures typically suffer from one or more of the following problems

This paper introduces a class of formal grammars made up by augmenting the formalism of context-free grammars with an explicit set-theoretic intersection operation.

Contents 1. Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.1 Grammars : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 1.2 Examples : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2. Systems of equations : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.1 Systems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.2 Resolution : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 11 2.3 Linear systems : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 12 2.4 Parikh's theorem : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :

We present the concepts of weighted language, ~ansduction and au-tomaton from algebraic automata theory as a general framework for describing and implementing decoding cascades in speech and lan-guage processing. This generality allows us to represent uniformly such information sources as pronunciation dictionaries, language models artd lattices, and to use uniform algorithms for building de-coding stages and for optimizing and combining them. In particular, a single automata join algorithm can be used either to combine in-formation sources such as a pronunciation dictionary and a context-dependency model during the construction of a decoder, or dynam-ically during the operation of the decoder. Applications to speech recognition and to Chinese text segmentation will be discussed. 1.

This paper revisits the theory of regular inference, in particular by extending the definition of structural completeness of a positive sample and by demonstrating two basic theorems. This framework enables to state the regular inference problem as a search through a boolean lattice built from the positive sample. Several properties of the search space are studied and generalization criteria are discussed. In this framework, the concept of border set is introduced, that is the set of the most general solutions excluding a negative sample. Finally, the complexity of regular language identification from both a theoritical and a practical point of view is discussed. 1 Introduction Regular inference is the process of learning a regular language from a set of examples, consisting of a positive sample, i.e. a finite subset of a regular language. A negative sample, i.e. a finite set of strings not belonging to this language, may also be available. This problem has been studied as early as th...