Abstract not found

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.

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.

m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

We describe the algorithmic and software design principles of an object-oriented library for weighted finite-state transducers. By taking advantage of the theory of rational power series, we were able to achieve high degrees of generality, modularity and irredundancy, while attaining competitive efficiency in demanding speech processing applications involving weighted automata of more than 10^7 states and transitions. Besides its mathematical foundation, the design also draws from important ideas in algorithm design and programming languages: dynamic programming and shortest-paths algorithms over general semirings, object-oriented programming, lazy evaluation and memoization.

Stochastic approaches to natural language processing have often been preferred to rule-based approaches because of their robustness and their automatic training capabilities. This was the case for part-of-speech tagging until Brill showed how state-of-the-art part-of-speech tagging can be achieved with a rule-based tagger by inferring rules from a training corpus. However, current implementations of the rule-based tagger run more slowly than previous approaches. In this paper, we present a finite-state tagger, inspired by the rule-based tagger, that operates in optimal time in the sense that the time to assign tags to a sentence corresponds to the time required to follow a single path in a deterministic finite-state machine. This result is achieved by encoding the application of the rules found in the tagger as a nondeterministic finite-state transducer and then turning it into a deterministic transducer. The resulting deterministic transducer yields a part-of-speech tagger whose speed is dominated by the access time of mass storage devices. We then generalize the techniques to the class of transformation-based systems. 1.

Many ofthe processing steps in natural language engineering can be performed using nite state transducers. An optimal way tocreate such transducers is to compile them from regular expressions. This paper is an introduction to the regular expression calculus, extended with certain operators that have proved very useful in natural language applications ranging from tokenization to light parsing. The examples in the paper illustrate in concrete detail some of these applications. 1

this paper are the following: (1) Lexical representations tend to be arbitrary. Because it is difficult to write and test two-level systems that map between pairs of radically dissimilar forms, lexical representations in existing two-level analyzers tend to stay close to the surface forms. This is not a problem for morphologically simple languages like English because, for most words, inflected forms are very similar to the canonical dictionary entry. Except for a small number of irregular verbs and nouns, it is not difficult to create a two-level description for English in which lexical forms coincide with the canonical citation forms found in a dictionary. However, current analyzers for morphologically more complex languages (Finnish and Russian, for example) are not as satisfying in this respect. In these systems, lexical forms typically contain diacritic markers and special symbols; they are not real words in the language. For example, in Finnish the lexical counterpart of otin `I took' might be rendered as

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

Much recent research on the design of natural language lexicons has made use of nonmonotonic inheritance networks as originally developed for general knowledge representation purposes in Artificial Intelligence. DATR is a simple, spartan language for de ning nonmonotonic inheritance networks with path/value equations, one that has been designed specifically for lexical knowledge representation. In keeping with its intendedly minimalist character, it lacks many of the constructs embodied either in general purpose knowledge representation languages or in contemporary grammar formalisms. The present paper shows that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of linguistic analysis. The paper provides an informal example-based introduction to DATR and to techniques for its use, including finite state transduction, the encoding of DAGs and lexical rules, and the representation of ambiguity and alternation. Sample analyses of phenomena such as inflectional syncretism and verbal subcategorisation are given which show how the language can be used to squeeze out redundancy from lexical descriptions.