This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.
The simplest part-of-speech taggers are bigram or trigram models @cite_12 @cite_1 . They require a relatively large tagged training text. Transformation-based tagging as introduced by also requires a hand-tagged text for training. No pretagged text is necessary for Hidden Markov Models @cite_7 @cite_13 @cite_2 . Still, a lexicon is needed that specifies the possible parts of speech for every word. have shown that the effort necessary to construct the part-of-speech lexicon can be considerably reduced by combining learning procedures and a partial part-of-speech categorization elicited from an informant.
@cite_7 @cite_1 @cite_2 @cite_13 @cite_12
2100796029 2166394306 2046224275 1509596266
Abstract A system for part-of-speech tagging is described. It is based on a hidden Markov model which can be trained using a corpus of untagged text. Several techniques are introduced to achieve robustness while maintaining high performance. Word equivalence classes are used to reduce the overall number of parameters in the model, alleviating the problem of obtaining reliable estimates for individual words. The context for category prediction is extended selectively via predefined networks, rather than using a uniformly higher-order conditioning which requires exponentially more parameters with increasing context. The networks are embedded in a first-order model and network structure is developed by analysis of erros, and also via linguistic considerations. To compensate for incomplete dictionary coverage, the categories of unknown words are predicted using both local context and suffix information to aid in disambiguation. An evaluation was performed using the Brown corpus and different dictionary arrangements were investigated. The techniques result in a model that correctly tags approximately 96 of the text. The flexibility of the methods is illustrated by their use in a tagging program for French. We derive from first principles the basic equations for a few of the basic hidden-Markov-model word taggers as well as equations for other models which may be novel (the descriptions in previous papers being too spare to be sure). We give performance results for all of the models. The results from our best model (96.45 on an unused test sample from the Brown corpus with 181 distinct tags) is on the upper edge of reported results. We also hope these results clear up some confusion in the literature about the best equations to use. However, the major purpose of this paper is to show how the equations for a variety of models may be derived and thus encourage future authors to give the equations for their model and the derivations thereof. We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96 . We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment. A consideration of problems engendered by the use of concordances to study additional word senses. The use of factor analysis as a research tool in lexicography is discussed. It is shown that this method provides information not obtainable through other approaches. This includes provision of several major senses for each word, an indication of the relationship between collocational patterns, & a more detailed analysis of the senses themselves. Sample factor analyses for the collocates of certain & right are presented & discussed. 3 Tables, 11 References. B. Annesser Murray