![]() ![]() Research Report 104, Eidgenossische Technische Hochschule (ETH), CH-8091 Zürich, Switzerland, Seminar fur Statistik (2002)Īlves, C.D.C., Finger, M.: Etiquetagem do português clássico baseada em córpora. Mächler, M., Bühlmann, P.: Variable length markov chains: Methodology, computing and software. In: Proceedings of ACL 32nd (1994)īühlmann, P., Wyner, A.J.: Variable length markov chains. Schütze, H., Singer, Y.: Part-of-speech tagging using a variable memory markov model. ![]() This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Future researches in statistical linguistics regarding long range dependencies should concentrate in other ways of solving this limitation. ![]() However, long distance dependencies are not well captured by the VLMC tagger, and we investigate the reasons and limitations of the use of VLMCs. More surprisingly, we did that with a total time of training and execution of less than 3 minutes for a corpus of almost 1 million words. We obtained one of the best PoS tagging of Portuguese, with a precision of 95.51%. In this work, we have employed Variable Length Markov Chains (VLMC) for tagging, in the hope of capturing long distance dependencies. Tagging is the task of attributing to words in context in a text, their corresponding Part-of-Speech (PoS) class. ![]()
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