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Abstract : |
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 nite-state tagger inspired by the rule-based tagger which 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 nite-state machine. This result is achieved by encoding the application of the rules found in the tagger as a non-deterministic nite-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., |
