Digital Library[ Search Result ]
Relation Extraction based on Neural-Symbolic Structure
http://doi.org/10.5626/JOK.2021.48.5.533
Deep learning has been continually demonstrating excellent performance in the field of natural language processing. However, enormous training data and long training time are required to achieve good performance. Herein, we propose a method that exceeds deep learning performance in a small learning data environment by using a neural-symbolic method for the relationship extraction problem. We have designed a structure that uses the inconsistency between the rule results and deep learning results. In addition, logical rule filtering has been proposed to improve the convergence speed and a context has been added to improve the performance of the rule. The proposed method showed excellent performance for a small amount of training data, and we confirmed that fast performance convergence was achieved.
Multi-sense Word Embedding to Improve Performance of a CNN-based Relation Extraction Model
Sangha Nam, Kijong Han, Eun-kyung Kim, Sunggoo Kwon, Yoosung Jung, Key-Sun Choi
http://doi.org/10.5626/JOK.2018.45.8.816
The relation extraction task is to classify a relation between two entities in an input sentence and is important in natural language processing and knowledge extraction. Many studies have designed a relation extraction model using a distant supervision method. Recently the deep-learning based relation extraction model became mainstream such as CNN or RNN. However, the existing studies do not solve the homograph problem of word embedding used as an input of the model. Therefore, model learning proceeds with a single embedding value of homogeneous terms having different meanings; that is, the relation extraction model is learned without grasping the meaning of a word accurately. In this paper, we propose a relation extraction model using multi-sense word embedding. In order to learn multi-sense word embedding, we used a word sense disambiguation module based on the CoreNet concept, and the relation extraction model used CNN and PCNN models to learn key words in sentences.
Competition Relation Extraction based on Combining Machine Learning and Filtering
ChungHee Lee, YoungHoon Seo, HyunKi Kim
This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr