Turkish Constituent Chunking with Morphological and Contextual Features

Abstract

State-of-the-art phrase chunking focuses on English and shows high accuracy with very basic word features such as the word itself and the POS tag. In case of morphologically rich languages like Turkish, basic features are not sufficient. Moreover, phrase chunking may not be appropriate and the “chunk” term should be redefined for these languages. In this paper, the first study on Turkish constituent chunking using two different methods is presented. In the first method, we directly extracted chunks from the results of the Turkish dependency parser. In the second method, we experimented with a CRF-based chunker enhanced with morphological and contextual features using the annotated sentences from the Turkish dependency treebank. The experiments showed that the CRF-based chunking augmented with extra features outperforms the baseline chunker with basic features and dependency parser-based chunker. Overall, we developed a CRF-based Turkish chunker with an F-measure of 91.95 for verb chunks and 87.50 for general chunks.