Indonesian Shift-Reduce Constituency Parser Using Feature Templates & Beam Search Strategy

Abstract

In natural language processing, the syntactic analysis process (such as constituency parsing) is required to understand word context in the sentence. We propose a modification on using binarization technique alternative and feature multiplication factors for shift-reduce constituency parser using beam search approach and structured learning algorithm. Our modification in binarization technique is inspired from assorted tagging schemes in NER, while the feature multiplication factors is used to scale up our scoring system for beam search algorithm. For evaluation, we mainly used the new INACL Treebank (consisting 11,356 and 4,457 instances for training and test set), resulted 50.3% in f1-score. Our parser also compared with previous work by using the same training and test set for IDN-Treebank, resulted 74.0% in f1-score.