Digital Library[ Search Result ]
Deep Learning-based Text Classification Model for Poisonous Clauses Classification
Gihyeon Choi, Youngjin Jang, Harksoo Kim, Kwanwoo Kim
http://doi.org/10.5626/JOK.2020.47.11.1054
Most companies sign contracts based on the contract prior to executing the task. However, several problems can occur if the poisonous clauses are not identified before the contract is concluded. To prevent this problem, companies have an expert review the contract, but the service requires much time and money. If there is a system in which the poisonous clauses can be identified through prior review of the contract, the high cost and time incurred in reviewing the contract can be mitigated. Thus, this paper proposes a text classification model that identifies any poisonous clause in the contract by inputing each paragraph in the contract. To improve the classification performance of the proposed model, the importance of each sentence is calculated based on the relationship information between the sentence in the paragraph and the class to be classified, and classification is performed by reflecting it in each sentence. The proposed model showed the performance of the F1 score 84.51%p in experiments using actual contract data and the highest performance with the F1 score 93.64%p in experiments using the WOS-5736 dataset for the performance comparison with the existing text classification models.
Jamo Unit Convolutional Neural Network Based Automatic Classification of Frequently Asked Questions with Spelling Errors
Youngjin Jang, Harksoo Kim, Dongho Kang, Sebin Kim, Hyunki Jang
http://doi.org/10.5626/JOK.2019.46.6.563
Web and mobile users obtain the desired information using the frequently asked questions (FAQ) listed on the homepage. The FAQ system displays a query response candidate that is most similar to the input based on an information retrieval model. However, the information retrieval model depends on the index, and therefore, it is vulnerable to spelling errors in the sentence. This paper proposes a model applying the FAQ system to the sentence classifier, which minimizes the spelling errors. Using the embedded layer with jamo-based convolutional neural network, the spelling errors of the user input were reduced. The performance of the classifier was improved using class embedding and feed-forward neural network. As a result of 457 and 769 FAQ classifications, the Micro F1 score showed 81.32% p and 61.11% p performance, respectively. We used the sigmoid function to quantify the reliability of the model prediction.
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