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A Named-Entity Recognition Training Method Using Bagging-Based Bootstrapping
Yujin Jeong, Juae Kim, Youngjoong Ko, Jungyun Seo
http://doi.org/10.5626/JOK.2018.45.8.825
Most previous named-entity(NE) recognition studies have been based on supervised learning methods. Although supervised learning-based NE recognition has performed well, it requires a lot of time and cost to construct a large labeled corpus. In this paper, we propose an NE recognition training method that uses an automatically generated labeled corpus to solve this problem. Since the proposed method uses a large machine-labeled corpus, it can greatly reduce the time and cost needed to generate a labeled corpus manually. In addition, a bagging-based bootstrapping technique is applied to our method in order to correct errors from the machine-labeled data. As a result, experimental results show that the proposed method achieves the highest F1 score of 70.76% by adding the bagging-based bootstrapping technique, which is 5.17%p higher than that of the baseline system.
A Korean Community-based Question Answering System Using Multiple Machine Learning Methods
Sunjae Kwon, Juae Kim, Sangwoo Kang, Jungyun Seo
Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.
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