A neural approach for detecting inline mathematical expressions from scientific documents. Issue 4 (19th May 2020)

• Record Type: Journal Article
• Authors: Madisetty, Sreekanth; Maurya, Kaushal Kumar; Aizawa, Akiko; Desarkar, Maunendra Sankar
• Other Names: Chakraborty Tanmoy guestEditor.; Bhatia Sumit guestEditor.; Caragea Cornelia guestEditor.; Moreira Fernando guestEditor.; Rocha Álvaro guestEditor.; Dubey Ashwani Kumar guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-19
• Subjects: deep learning -- independent mathematical expression -- inline mathematical expression -- natural language processing -- scientific text mining; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12576
• Languages: English
• ISSNs: 0266-4720

Abstract

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.