Active Learning Yields Better Training Data for Scientific Named Entity Recognition

Tchoua, Roselyne; Ajith, Aswathy; Hong, Zhi; Ward, Logan; Chard, Kyle; Audus, Debra J.; Patel, Shrayesh N.; Pablo, Juan; Foster, Ian

doi:10.1109/escience.2019.00021

Cited by 11 publications

(11 citation statements)

References 36 publications

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“…There is no feedback between the data annotation phrase and the model training phase. Active learning breaks this boundary by actively selecting the samples that are sent to annotators for labeling . Compared with supervised learning, active learning can greatly reduce the number of training examples needed for the training process.…”

Section: Constructing the Benchmark Data Setmentioning

confidence: 93%

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Zhang

Wang

Mya

et al. 2022

J. Chem. Inf. Model.

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Valuable knowledge of catalysis is often hidden in a large amount of scientific literature. There is an urgent need to extract useful knowledge to facilitate scientific discovery. This work takes the first step toward the goal in the field of catalysis. Specifically, we construct the first information extraction benchmark data set that covers the field of catalysis and also develop a general extraction framework that can accurately extract catalysis-related entities from scientific literature with 90% extraction accuracy. We further demonstrate the feasibility of leveraging the extracted knowledge to help users better access relevant information in catalysis through an entity-aware search engine and a correlation analysis system.

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Section: Constructing the Benchmark Data Setmentioning

confidence: 93%

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Zhang

Wang

Mya

et al. 2022

J. Chem. Inf. Model.

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“…69 Researchers have also pursued active learning with maximum-entropy uncertainty sampling to achieve valuable annotations from experts to improve performance, but this proved time intensive to pursue. 72 Roles for hybrid systems also include establishing dictionaries for stop words and rules to detect systematic names.…”

Section: Named Entity Recognition (Ner)mentioning

confidence: 99%

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

Olivetti

Cole

Kim

et al. 2020

Applied Physics Reviews

178

103

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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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“…Recent advances in natural language processing have produced an increased interest in active learning to alleviate the requirement for large annotated corpora (Olsson, 2009;Tchoua et al, 2019). Settles and Craven (2008) compare several strategies for active learning in sequence labelling scenarios, concluding that query strategies based on measures of sequence entropy combined with weighted sampling outperform other variants.…”

Section: Related Workmentioning

confidence: 99%

Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text

Cañizares-Diaz¹,

Piad-Morffis²,

Estévez-Velarde³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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This paper presents an active learning approach that aims to reduce the human effort required during the annotation of natural language corpora composed of entities and semantic relations. Our approach assists human annotators by intelligently selecting the most informative sentences to annotate and then pre-annotating them with a few highly accurate entities and semantic relations. We define an uncertainty-based query strategy with a weighted density factor, using similarity metrics based on sentence embeddings. As a case study, we evaluate our approach via simulation in a biomedical corpus and estimate the potential reduction in total annotation time. Experimental results suggest that the query strategy reduces by between 35% and 40% the number of sentences that must be manually annotated to develop systems able to reach a target F 1 score, while the pre-annotation strategy produces an additional 24% reduction in the total annotation time. Overall, our preliminary experiments suggest that as much as 60% of the annotation time could be saved while producing corpora that have the same usefulness for training machine learning algorithms. An open-source computational tool that implements the aforementioned strategies is presented and published online for the research community.

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Active Learning Yields Better Training Data for Scientific Named Entity Recognition

Cited by 11 publications

References 36 publications

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

Unleashing the Power of Knowledge Extraction from Scientific Literature in Catalysis

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

Active Learning for Assisted Corpus Construction: A Case Study in Knowledge Discovery from Biomedical Text

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