A Comparative Study on Statistical Classification Methods in Relation Extraction
Zhang Xiaofeng, Gao Zhiqiang, Gui Yaocheng
Available Online November 2013.
- https://doi.org/10.2991/icmt-13.2013.21How to use a DOI?
- Relation Extraction, Named Entity Recognition, Statistical Classification
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 3rd International Conference on Multimedia Technology(ICMT-13) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icmt-13.2013.21 DO - https://doi.org/10.2991/icmt-13.2013.21 ID - Xiaofeng2013/11 ER -