Experimenting Oriya Text Chunking with Divide-Conquer Strategy

Abstract

The traditional oriya text chunking approach identifies phrase structure or local word group by using only one model and phrases with the same types of features. Generally oriya language is a free word order language. Free word order languages have relatively unrestricted local word group or phrase structures that make the problem of chunking quite challenging It has been shown that the limitations of using only one model are that: the use of the same types of features is not suitable for all phrases.. In this paper, the divide-conquer approach is proposed and applied in the identification of phrases or local word group. This strategy divides the task of chunking into several sub-tasks according to sensitive features of each phrase and identifies different phrases in parallel. Then, a two-stage decreasing conflict strategy is used to synthesize each sub-task's answer We argue that we might not need an explicit intermediate POS-tagging step for parsing when a sufficient amount of training material is available and word form information is used for low-frequency words. By applying and testing the approach on the public training and test corpus, the F score for arbitrary phrases identification using divide-conquer strategy achieves 91.3% compared to the previous best F score of 92.18%.