A neural approach for detecting inline mathematical expressions from scientific documents

Abstract

Scientific documents generally contain multiple mathematical expressions in them.Detecting inline mathematical expressions are one of the most important and challenging tasks in scientific text mining. Recent works that detect inline mathematical expressions in scientific documents have looked at the problem from an image processing perspective. There is little work that has targeted the problem from NLP perspective. Towards this, we define a few features and applied Conditional Random Fields (CRF) to detect inline mathematical expressions in scientific documents. Apart from this feature based approach, we also propose a hybrid algorithm that combines Bidirectional Long Short Term Memory networks (Bi-LSTM) and feature-based approach for this task. Experimental results suggest that this proposed hybrid method outperforms several baselines in the literature and also individual methods in the hybrid approach.

Modeling Mathematical Notation Semantics in Academic Papers

“…A complementary line of research takes notation as input, and attempts to predict text to describe the notation. In recent work, Abekawa and Aizawa (2016) retrieved a relevant paragraph to a given query, and Alexeeva et al (2020) extracted symbol descriptions from text, and Madisetty et al (2020) detected symbol descriptions in math equations. In contrast to prior work, our model takes text as input and predicts appropriate mathematical notation.…”

Special section on mining knowledge from scientific data

“…They also showed the effect of such outlier behaviour on the bibliographic metrics such as h-index and impact factor. Madisetty et al (2020) proposed a tool to extract inline mathematical expressions from scientific articles. This is a major problem in scientific document processing as mathematic systems often act as a bottleneck due to their cryptic symbols that a parse is unable to extract.…”