TnT tagger with fuzzy rule based learning

TnT is an efficient statistical Parts-of-speech (POS) Tagger based on Hidden Markov Model. TnT stands for Trigrams`n´Tags. Viterbi algorithm is used for finding the best tag sequence for a given observation sequence of words. TnT performs well on known word sequences. But, the performance degrades with increase in the number of unknown words. In this paper, we propose a method to overcome this performance degradation using fuzzy rules. Fuzzy rule based model is designed to provide TnT with sufficient information about the tag of unknown words without degrading the performance of TnT. When TnT with fuzzy rule based learning encounters an unknown word, the TnT generates a set of possible tags for the given word, based on the fuzzy rules matched by the word. If the word does not match any fuzzy rule, then the model depends upon the probability distribution of the suffix. This approach guarantees that the performance of TnT will only be improved from its normal performance.