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Information Extraction Using Distant Supervision and Semantic SimilaritiesPARK, Y.   , KANG, S. , SEO, J. |
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Author keywords
relation extraction, unsupervised learning, distant supervision, information extraction, natural language processing
References keywords
extraction(15), link(14), computational(12), relation(11), meeting(10), linguistics(10), association(10), supervision(7), distant(7), semantic(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2016-02-28
Volume 16, Issue 1, Year 2016, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.01002
Web of Science Accession Number: 000376995400002
SCOPUS ID: 84960113172
Abstract
Information extraction is one of the main research tasks in natural language processing and text mining that extracts useful information from unstructured sentences. Information extraction techniques include named entity recognition, relation extraction, and co-reference resolution. Among them, relation extraction refers to a task that extracts semantic relations between entities such as personal and geographic names in documents. This is an important research area, which is used in knowledge base construction and question and answering systems. This study presents relation extraction using a distant supervision learning technique among semi-supervised learning methods, which have been spotlighted in recent years to reduce human manual work and costs required for supervised learning. That is, this study proposes a method that can improve relation extraction by improving a distant supervision learning technique by applying a clustering method to create a learning corpus and semantic analysis for relation extraction that is difficult to identify using existing distant supervision. Through comparison experiments of various semantic similarity comparison methods, similarity calculation methods that are useful to relation extraction using distant supervision are searched, and a large number of accurate relation triples can be extracted using the proposed structural advantages and semantic similarity comparison. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
