Voted-Perceptron Approach for Kazakh Morphological Disambiguation
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
Abstract
This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.