A Multi-Neuro Tagger Using Variable Lengths of Contexts

概要

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This paper presents a multi-neuro tagger that uses variable lengths of contexts for part of speech (POS) tagging based on longest context priority. The tagger is constructed using multiple neural networks, all of which can be regarded as single-neuro taggers with fixed but different lengths of contexts in inputs, and the longest context priority based selector. Because the trained results (weights) of the taggers with shorter lengths of contexts can be used as initial weights for those with longer lengths of contexts, the training time for the latter ones can be greatly reduced and the cost to train a multi-neuro tagger is almost the same as that to train a single-neuro tagger. In tagging, given that the target word is more relevant than any of the words in its context and the words in context may have different relevances, each element of the input is weighted by its relevance with information gain. Computer experiments show that the multi-neuro tagger has a correct rate of over 94% for tagging untrained data when a small Thai corpus with 8, 322 sentences that we have on hand is used for training. This result is better than any of the results obtained using the single-neuro taggers, which indicates that the multi-neuro tagger can dynamically find a suitable length of contexts in tagging.
言語処理学会の論文