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Transforming graph-based sentence representations to alleviate overfitting in relation extraction

Published: 16 September 2014 Publication History

Abstract

Relation extraction (RE) aims at finding the way entities, such as person, location, organization, date, etc., depend upon each other in a text document. Ontology Population, Automatic Summarization, and Question Answering are fields in which relation extraction offers valuable solutions. A relation extraction method based on inductive logic programming that induces extraction rules suitable to identify semantic relations between entities was proposed by the authors in a previous work. This paper proposes a method to simplify graph-based representations of sentences that replaces dependency graphs of sentences by simpler ones, keeping the target entities in it. The goal is to speed up the learning phase in a RE framework, by applying several rules for graph simplification that constrain the hypothesis space for generating extraction rules. Moreover, the direct impact on the extraction performance results is also investigated. The proposed techniques outperformed some other state-of-the-art systems when assessed on two standard datasets for relation extraction in the biomedical domain.

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Cited By

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  • (2020)An Assessment of Sentence Simplification Methods in Extractive Text SummarizationProceedings of the ACM Symposium on Document Engineering 202010.1145/3395027.3419588(1-9)Online publication date: 29-Sep-2020
  • (2019)A logic-based relational learning approach to relation extraction: The OntoILPER systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2018.11.00178(142-157)Online publication date: Feb-2019
  • (2018)Assessing Sentence Simplification Methods Applied to Text Summarization2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00017(49-54)Online publication date: Oct-2018
  • Show More Cited By

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cover image ACM Conferences
DocEng '14: Proceedings of the 2014 ACM symposium on Document engineering
September 2014
226 pages
ISBN:9781450329491
DOI:10.1145/2644866
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 September 2014

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Author Tags

  1. graph-based model
  2. inductive logic programming
  3. relation extraction
  4. sentence simplification

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  • Research-article

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  • Hewlett-Packard Brazil & UFPE

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DocEng '14
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DocEng '14: ACM Symposium on Document Engineering 2014
September 16 - 19, 2014
Colorado, Fort Collins, USA

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DocEng '14 Paper Acceptance Rate 15 of 41 submissions, 37%;
Overall Acceptance Rate 194 of 564 submissions, 34%

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Cited By

View all
  • (2020)An Assessment of Sentence Simplification Methods in Extractive Text SummarizationProceedings of the ACM Symposium on Document Engineering 202010.1145/3395027.3419588(1-9)Online publication date: 29-Sep-2020
  • (2019)A logic-based relational learning approach to relation extraction: The OntoILPER systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2018.11.00178(142-157)Online publication date: Feb-2019
  • (2018)Assessing Sentence Simplification Methods Applied to Text Summarization2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00017(49-54)Online publication date: Oct-2018
  • (2018)OntoILPERKnowledge and Information Systems10.1007/s10115-017-1108-356:1(223-255)Online publication date: 1-Jul-2018

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