A neural approach for detecting inline mathematical expressions from scientific documents

Madisetty, Sreekanth and Maurya, Kaushal Kumar and Aizawa, Akiko and Desarkar, Maunendra Sankar (2021) A neural approach for detecting inline mathematical expressions from scientific documents. Expert Systems, 38 (4). ISSN 0266-4720

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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.

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IITH Creators:
IITH CreatorsORCiD
Madisetty, SreekanthUNSPECIFIED
Maurya, Kaushal KumarUNSPECIFIED
Desarkar, Maunendra SankarUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Conditional random field; Feature based approaches; Hybrid algorithms; Hybrid approach; Mathematical expressions; Scientific documents; Scientific texts; Short term memory;Image processing; Natural language processing systems; Random processes; Text mining
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 10 Aug 2021 04:13
Last Modified: 10 Aug 2021 04:13
URI: http://raiith.iith.ac.in/id/eprint/8777
Publisher URL: http://doi.org/10.1111/exsy.12576
OA policy: https://v2.sherpa.ac.uk/id/publication/6806
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