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Unsupervised Identification of Study Descriptors in Toxicology Research: An Experimental Study...

Publication Type
Conference Paper
Journal Name
International Workshop on Health Text Mining and Information Analysis (LOUHI)
Publication Date
Page Numbers
71 to 82
Conference Name
9th International Workshop on Health Text Mining and Information Analysis (LOUHI 2018) at the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)
Conference Location
Brussels, Belgium
Conference Sponsor
Association for Computational Linguistics (ACL) Special Interest Group on Linguistic Data and Corpus-Based Approaches to NLP (SIGDAT)
Conference Date

Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.