The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

• URL: http://arxiv.org/abs/2006.03039v1
• Date: Thu, 4 Jun 2020 17:49:34 GMT
• Title: The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
• Authors: Annemarie Friedrich and Heike Adel and Federico Tomazic and Johannes Hingerl and Renou Benteau and Anika Maruscyk and Lukas Lange
• Abstract summary: We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition. We present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set.
• Score: 11.085048329202335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.

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