The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction.
EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. 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. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. Publisher = "Association for Computational Linguistics",Ībstract = "Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements.
Textual evidence mods#
Cite (Informal): EVIDENCEMINER: Textual Evidence Discovery for Life Sciences (Wang et al., ACL 2020) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Video: = ": Textual Evidence Discovery for Life Sciences",īooktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations", Association for Computational Linguistics. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 56–62, Online. EVIDENCEMINER: Textual Evidence Discovery for Life Sciences. Anthology ID: 2020.acl-demos.8 Volume: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations Month: July Year: 2020 Address: Online Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 56–62 Language: URL: DOI: 10.18653/v1/2020.acl-demos.8 Bibkey: wang-etal-2020-evidenceminer Cite (ACL): Xuan Wang, Yingjun Guan, Weili Liu, Aabhas Chauhan, Enyi Jiang, Qi Li, David Liem, Dibakar Sigdel, John Caufield, Peipei Ping, and Jiawei Han.
The system of EVIDENCEMINER is available at. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. The annotation results are also highlighted in the original document for better visualization.
Abstract Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements.
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