Can symbol grounding improve low-level NLP? Word segmentation as a case study

Abstract

We propose a novel framework for improving a word segmenter using information acquired from symbol grounding. We generate a term dictionary in three steps: generating a pseudo-stochastically segmented corpus, building a symbol grounding model to enumerate word candidates, and filtering them according to the grounding scores. We applied our method to game records of Japanese chess with commentaries. The experimental results show that the accuracy of a word segmenter can be improved by incorporating the generated dictionary.