Concerning different approaches to automatic PoS tagging: EngCG-2, a constraintbased morphological tagger, is compared in a double-blind test with a state-of-the-art statistical tagger on a common disambiguation task using a common tag set. The ex- periments show that for the same amount

Abstract. We propose a method for providing stochastic confidence estimates for rule-based and black-box natural language (NL) processing systems. Our method does not require labeled training data: We simply train stochastic models on the output of the original NL systems. Numeric confidence

to stochastic taggers. The rule-based tagger has many advantages over these taggers, including: a vast reduction in stored information required, the perspicuity of a sinall set of meaningful rules, ease of finding and implementing improvements to the tagger, and better portability from one tag set, cor- pus

;/supplement> Background: Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system [1,2]. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Where their system uses the Brill tagger, we used TnT, the Trigrams 'n &apos

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger

reconfigurable stochastic tagger for languages with complex tag structure

do not necessarily involve trading off accuracy against speed—they may in fact be more accurate than exhaustive-search stochastic contextfree parsers. 1 Finite-State Cascades Of current interest in corpus-oriented computational linguistics are techniques for bootstrapping broad-coverage parsers from

This paper presents a new method for producing a dictionary of subcategorization frames from un- labelled text corpora. It is shown that statistical filtering of the results of a finite state parser running on the output of a stochastic tagger produces high quality results, despite the error rates

An efficient implementation of a part-of-speech tagger for Swedish is described. The stochastic tagger uses a well-established Markov model of the language. The tagger tags 92% of unknown words correctly and up to 97% of all words. Several implementation and optimization considerations

Abstract. We introduce an implementation of a plain trigram part-of-speech tagger which appears to work well on Polish texts. At this moment the tagger achieves 9.4 % error rate, which makes it signficantly better than our previous stochastic disambiguator. Since the trigram model for Polish