Finite-state machines have been used in various domains of natural language processing. We consider here the use of a type of transducers that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential string-tostring transducers. Transducers that output weights also play an important role in language and speech processing. We give a specific study of string-to-weight transducers, including algorithms for determinizing and minimizing these transducers very efficiently, and characterizations of the transducers admitting determinization and the corresponding algorithms. Some applications of these algorithms in speech recognition are described and illustrated. 1.

Context-dependent rewrite rules are used in many areas of natural language and speech processing. Work in computational phonology has demonstrated that, given certain conditions, such rewrite rules can be represented as finite-state transducers (FSTs). We describe a new algorithm for compiling rewrite rules into FSTs. We show the algorithm to be simpler and more efficient than existing algorithms. Further, many

Syntactic constraints in Koskenniemi's Finite-State Intersection Granunar (FSIG) are logically less complex than their formalism (Koskenniemi et al., 1992) would suggest: It turns out that although the constraints in Voutilainen's (1994) FSIG description of English make use of several extensions to regular expressions, the description as a whole reduces to a finite combination of union, complement and concatenation.

Text and speech processing: hard problems Theory of automata Appropriate level of abstraction