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A neural approach for detecting inline mathematical expressions from scientific documents
Author(s) -
Madisetty Sreekanth,
Maurya Kaushal Kumar,
Aizawa Akiko,
Desarkar Maunendra Sankar
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12576
Subject(s) - computer science , perspective (graphical) , feature (linguistics) , artificial intelligence , task (project management) , artificial neural network , conditional random field , machine learning , philosophy , linguistics , management , economics
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.