EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
Date
2020-07
Authors
Wang, Xuan
Guan, Yingjun
Liu, Welli
Chauhan, Aabhas
Jiang, Enyi
Liem, David
Sigdel, Dibakar
Caulfield, J. Harry
Ping, Peipei
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Association for Computational Linguistics
Abstract
Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. The system of EVIDENCEMINER is available at https://evidenceminer.firebaseapp.com/.
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This proceeding is published as Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, and Jiawei Han. 2020. EVIDENCEMINER: Textual Evidence Discovery for Life Sciences. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 56–62, Online. Association for Computational Linguistics. doi:10.18653/v1/2020.acl-demos.8.