Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.

An Analysis of Simple Data Augmentation for Named Entity Recognition

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains—enabled by techniques adapted from the field of natural language processing—therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

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Data-driven materials research enabled by natural language processing and information extraction

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

https://escholarship.org/content/qt1mk883j5/qt1mk883j5.pdf?t=qpzz3k

Opportunities and challenges of text mining in aterials research.

http://www.cell.com/article/S2666389922000733/pdf

Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science

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.

/pdf/the-sofc-exp-corpus-and-neural-approaches-to-information-48n9ag3xxs.pdf

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

Materials Science Knowledge: Finding Diamonds in the Dirt. For manufacturing companies, employing new and innovative materials is crucial for developing competitive products. There are thousands of materials available for production purposes within the automotive industry, for consumer goods, energy solutions, or building technology, all fields in which the Robert Bosch GmbH is an active player. In order to respond to new demanding requests from the market or regulatory organisations, Bosch engineers constantly introduce new materials that meet complex requirements. Developing new materials critically depends on the ability to find high quality answers about existing materials in a timely manner. In the last decades, there has been an exponential growth in the volume of information about new materials and chemical components, with thousands of new papers and patents appearing every year. Analyzing this data and finding information relevant to a concrete need is a challenging task for materials engineers and researchers. For example, the following query expresses such an information need: “Find anode materials in Intermediate Temperature Solid Oxide Fuel Cells (IT-SOFC) that produce high power density.”

Anika Marusczyk

Papers

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

Towards the Bosch Materials Science Knowledge Base.