A Comparative Study on Statistical Classification Methods in Relation Extraction

DOI

10.2991/icmt-13.2013.21How to use a DOI?

Keywords

Relation Extraction, Named Entity Recognition, Statistical Classification

Abstract

This paper is a comparative study of statistical classification approaches in relation extraction and classification. The focus is on multiclass classification, not on sequence labeling. Five methods are evaluated, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (kNN), support vector machine (SVM) and sparse network of Winnow (SNoW). Using DT on Roth and Yih data set, the best precision and recall are achieved on both tasks of named entity recognition (NER) and relation extraction (RE). SNoW is not so good as DT, but it performs better than the other approaches. SVM performs better on precision and worse on recall. In contrast, the simplest methods NB and kNN has relative poor performance but they are not sensitive to learning tasks and classes.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhang Xiaofeng
AU  - Gao Zhiqiang
AU  - Gui Yaocheng
PY  - 2013/11
DA  - 2013/11
TI  - A Comparative Study on Statistical Classification Methods in Relation Extraction
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 167
EP  - 174
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.21
DO  - 10.2991/icmt-13.2013.21
ID  - Xiaofeng2013/11
ER  -