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A Neural Network Approach to Part-of-Speech Tagging *

Neural networks are one of the most efficient techniques for learning from scarce data. This property is very useful when trying to build a part-of-speech tagger. Available part-of-speech taggers need huge amounts of hand tagged text, but for Portuguese there is no such corpora available. In this paper we propose a neural network that, apparently, is capable of overcoming the huge training corpus problem. Distinct network topologies are applied to the problem of learning the parameters of a part-of-speech tagger from a very small Portuguese training corpus and from a subset of the Susanne Corpus. The experiments carried out are discussed. The results obtained point to a correction rate above the 97% starting in with a hand tagged training corpus with approximately 15,000 words.