Proceedings of the 2016 4th International Conference on Advanced Materials and Information Technology Processing (AMITP 2016)

A Similarity-Based Method for Entity Coreference Resolution in Big Data Environment

Authors
Yushui Geng, Peng Li, Jing Zhao
Corresponding Author
Yushui Geng
Available Online September 2016.
DOI
https://doi.org/10.2991/amitp-16.2016.22How to use a DOI?
Keywords
big data, entity coreference resolution, similarity, MapReduce.
Abstract
Processing and analyzing large scale data is needed in the big data environment, however, a large number of duplicate data refer to the same entity in the data set have brought great difficulties to analyze and process the acquired data. The method based on cluster analysis is one of the main methods of entity coreference resolution, but it is time-consuming and does not apply to big data environment. This paper presents a similarity-based method for entity coreference resolution by introducing weight and similarity and using Hadoop platform and MapReduce framework, which will process data into the form of key-value data pairs and can be efficiently applied to the entity coreference resolution. Experiments show that the proposed method greatly improves the speed and accuracy of entity coreference resolution, meets the demand for entity coreference resolution in big data environment.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
September 2016
ISBN
978-94-6252-245-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/amitp-16.2016.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yushui Geng
AU  - Peng Li
AU  - Jing Zhao
PY  - 2016/09
DA  - 2016/09
TI  - A Similarity-Based Method for Entity Coreference Resolution in Big Data Environment
PB  - Atlantis Press
SP  - 110
EP  - 116
SN  - 2352-538X
UR  - https://doi.org/10.2991/amitp-16.2016.22
DO  - https://doi.org/10.2991/amitp-16.2016.22
ID  - Geng2016/09
ER  -