Abstract not found

Although adequate models of human language for syntactic analysis and semantic interpretation are of at least context- free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred. These requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approxima- tion which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing. A method is presented here for calculating such finite-state approximations from context-free grammars. It is essentially different from the algorithm introduced by Pereira and Wright (1991; 1996), is faster in some cases, and has the advantage of being open-ended and adaptable.

Abstract not found

We consider the problem of computing the Kullback-Leibler distance, also called the relative entropy, between a probabilistic context-free grammar and a probabilistic finite automaton. We show that there is a closed-form (analytical) solution for one part of the Kullback-Leibler distance, viz. the cross-entropy. We discuss several applications of the result to the problem of distributional approximation of probabilistic context-free grammars by means of probabilistic finite automata. 1

Consulting a dictionary for the words of a given text provides multiple solutions, that is, ambiguities; thus, the sequence of words pilot studies cotfid lead for exanple to: pilot: N singular, V infinitive, V (conjugated) studies: N plural, V (conjugated) pilot studies: N plural (compound).

A conservative extension of standard spelling correction systems for German is discussed which goes beyond normal checking of isolated single words by taking multi-words, linguistically motivated non-words, as well as contexts into account.