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Algorithms 2017, 10(2), 42; doi:10.3390/a10020042

RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation

Department of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Academic Editor: Evangelos Kranakis
Received: 10 January 2017 / Revised: 21 March 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
View Full-Text   |   Download PDF [1247 KB, uploaded 17 April 2017]   |  


Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities. View Full-Text
Keywords: distributional relation representation; co-occurrence matrix; Neo4j; global vectors; cosine similarity distributional relation representation; co-occurrence matrix; Neo4j; global vectors; cosine similarity

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, Z.; Huang, Y.; Liang, Y.; Wang, Y.; Fu, X.; Fu, K. RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation. Algorithms 2017, 10, 42.

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