Part-of-Speech Tagging Without Training

Abstract

The development of the Internet and the World Wide Web can be either a threat to the survival of indigenous languages or an opportunity for their development. The choice between cultural diversity and linguistic uniformity is in our hands and the outcome depends on our capability to devise, design and use tools and techniques for the processing of natural languages. Unfortunately natural language processing requires extensive expertise and large collections of reference data. Our research is concerned with the economical and therefore semi-automatic or automatic acquisition of such linguistic information necessary for the development of indigenous or multilingual information systems. In this paper, we propose new methods and variants of existing methods for part-of- speech tagging. We comparatively and empirically analyze the proposed methods and existing reference methods using the Brown English language corpus and we present some preliminary remarks on experiments with an Indonesian language Corpus.KeywordsHide Markov ModelFrequent WordNatural Language ProcessingIndigenous LanguageContext VectorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.