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Title: Scholarly Big Data: Computational Approaches to Semantic Labeling in Materials Science
This paper explores computational, semantic labeling for scholarly big data in materials science. We report on a baseline comparative analysis involving ontology-based automatic indexing with the Helping Interdisciplinary Vocabulary Engineering (HIVE-4-MAT) application, using the RAKE algorithm, and the MATScholar system, which uses named entity recognition (NER), supported by an RNN (Recursive Neural Network). Results demonstrate that ontology-based automatic indexing requires less preparation time and provides useful output supporting recall; while NER/RNN requires greater preparation, but produces more precise labels that are likely better for deep learning.  more » « less
Award ID(s):
1940239
PAR ID:
10185098
Author(s) / Creator(s):
Date Published:
Journal Name:
IEEEACM Joint Conference on Digital Libraries JCDL
ISSN:
2575-7865
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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