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A Flexible Text Mining System for Entity and Relation Extraction in PubMed

Published: 22 October 2015 Publication History

Abstract

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Therefore, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Extending Stanford CoreNLP, we developed the system with multiple types of entities and relations.
We demonstrate the performance by evaluating on various corpora such as CRAFT, GENETAG, AnEM Corpus, NCBI Disease Corpus, DDI Corpus, Metabolite and Enzyme Corpus for NER and BioInfer, AIMed, GAD, CoMAGC, and PolySearch for RE and achieve with average F-measures of 85% for entity extraction and 82% for relation extraction. As advantages of this system, one is a configurability in various combinations of text-processing components that can be plugged in for different tasks. The other is an extensible framework for extraction; extensible rule engine for relation extraction (Plug-and-play approach).
As shown in figure 1, the system contains two major pipelines for public knowledge discovery. The first pipeline extracts target entities based on dictionaries by extending the Stanford CoreNLP. The second pipeline applies dependency tree-based rules to sentences with two or more entities to extract relationships among those entities.

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  • (2019)Identification of Conclusive Association Entities by Biomedical Association MiningIntelligent Information and Database Systems10.1007/978-3-030-14799-0_9(103-114)Online publication date: 7-Mar-2019

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  1. A Flexible Text Mining System for Entity and Relation Extraction in PubMed

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    cover image ACM Conferences
    DTMBIO '15: Proceedings of the ACM Ninth International Workshop on Data and Text Mining in Biomedical Informatics
    October 2015
    40 pages
    ISBN:9781450337878
    DOI:10.1145/2811163
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 22 October 2015

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    Author Tags

    1. information extraction
    2. named entity recognition
    3. relation extraction
    4. text mining

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    • The Ministry of Science ICT and Future Planning through the National Research Foundation

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    CIKM'15
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    Overall Acceptance Rate 41 of 247 submissions, 17%

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    Cited By

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    • (2019)Identification of Conclusive Association Entities by Biomedical Association MiningIntelligent Information and Database Systems10.1007/978-3-030-14799-0_9(103-114)Online publication date: 7-Mar-2019

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