Informal mathematical discourse parsing with conditional random fields

Abstract

Discourse parsing for the Informal Mathematical Discourse (IMD) has been a difficult task because of the lack of data sets, partly because the Natural Language Processing (NLP) techniques must be adapted to informality of IMD. In this paper, we present an end-to-end discourse parser which is a sequential classifier of informal deductive argumentations (IDA) for Spanish. We design a discourse parser using sequence labeling based on CRFs (Conditional Random Fields). We use the CRFs on lexical, syntactic and semantic features extracted from a discursive corpus (MD-TreeBank: Mathematical Discourse Tree Bank). In this article, we describe a Penn Discourse Tree Bank (PDTB) styled End-to-End discourse parser into the Control Natural Languages (CNLs) context. Discourse parsing is focused from a discourse low level perspective in which we identify the IDA connectives avoiding complex linguistic phenomena. Our discourse parser performs parsing as a connective-level sequence labeling task and classifies several types of informal deductive argumentations into the mathematical proof.