Special Techniques for Constituent Parsing of Morphologically Rich Languages

Abstract

We introduce three techniques for improving constituent parsing for morphologically rich languages. We propose a novel approach to automatically find an optimal preterminal set by clustering morphological feature values and we conduct experiments with enhanced lexical models and feature engineering for rerankers. These techniques are specially designed for morphologically rich languages (but they are language-agnostic). We report empirical results on the treebanks of five morphologically rich languages and show a considerable improvement in accuracy and in parsing speed as well.