Definition Extraction from Generic and Mathematical Domains with Deep Ensemble Learning

Natalia Vanetik and Marina Litvak
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Natalia Vanetik: Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva 8434231, Israel
Marina Litvak: Software Engineering Department, Shamoon College of Engineering, Bialik 56, Beer Sheva 8434231, Israel

Mathematics, 2021, vol. 9, issue 19, 1-18

Abstract: Definitions are extremely important for efficient learning of new materials. In particular, mathematical definitions are necessary for understanding mathematics-related areas. Automated extraction of definitions could be very useful for automated indexing educational materials, building taxonomies of relevant concepts, and more. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. In this paper, we focus on automatic detection of one-sentence definitions in mathematical and general texts. We experiment with different classification models arranged in an ensemble and applied to a sentence representation containing syntactic and semantic information, to classify sentences. Our ensemble model is applied to the data adjusted with oversampling. Our experiments demonstrate the superiority of our approach over state-of-the-art methods in both general and mathematical domains.

Keywords: definition extraction; deep learning; ensemble; mathematical domain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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