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A Verb-based Algorithm for Multiple-Relation Extraction from Single Sentences

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationProceddings of the International Conference on Information and Knowledge Engineering
PublisherCSREA Press Inc.
Pages115
Number of pages7
ISBN (Print)1-60132-463-4
Published2017

Documents

  • IKE3227

    IKE3227.pdf, 81.6 KB, application/pdf

    Uploaded date:14 Feb 2018

    Version:Final published version

King's Authors

Abstract

With the growing amount of unstructured articles
written in natural-language, automated extracting
knowledge of associations between entities is becoming
essential for many applications. In this paper, we develop
automated verb-based algorithm for multiple-relation extraction
from unstructured data obtained on-line. Named
Entity Recognition (NER) techniques were applied to extract
biomedical entities and relations were recognized by algorithms
with Natural Language Processing (NLP) techniques.
Evaluation based on F-measure with random sample of
sentences from biomedical literature results an average
precision of 90% and recall of 82%. We also compared the
performance of proposed algorithm with single-relation extraction
algorithm, indicating improvements of this work. In
conclusion, the preliminary study indicates that this method
for multiple-relation extraction from unstructured literature
is effective. With different training dataset, the algorithm can
be applied to different domains. The automated method can
be applied to detect and predict hidden relationships among
varying areas

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