Math Chunking and Function Recognition using Deep Learning

Abstract

In machine learning applications, mapping math knowledge from the series of tokens in a formula or expression to their linguistic semantic meaning remains an open area of research. One fundamental task towards that end is the chunking of a math equation/expression into meaningful math entities. It is the equivalent of sentence segmentation or chunking in natural language processing. Math chunking is quite broad and in a nascent stage in math linguistics. In this paper, we begin an exploration into this task using deep learning on a focused part of chunking, namely, recognition of functions (along with their arguments and parameters), in input equations. Specifically, we propose math-chunking models to identify a list of standard functions. We further develop an annotated dataset to train and evaluate our models. Our experimental results show that one of our proposed deep learning models, namely BiLSTM-CRF, can achieve rather high state-of-the-art performance on the mathematical formula chunking task.