Browse > Article
http://dx.doi.org/10.13088/jiis.2020.26.3.109

A Study on the Establishment of Comparison System between the Statement of Military Reports and Related Laws  

Jung, Jiin (Department of Industrial Engineering, Yonsei University)
Kim, Mintae (Department of Industrial Engineering, Yonsei University)
Kim, Wooju (Department of Industrial Engineering, Yonsei University)
Publication Information
Journal of Intelligence and Information Systems / v.26, no.3, 2020 , pp. 109-125 More about this Journal
Abstract
The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.
Keywords
Defence Acquisition Program; Sentence Similarity; Natural Language Processing; Siamese Network; Self-Attention;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Bahdanau, D., K. H. Cho, Y. Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate," ICLR(2015).
2 Bromley, J., I. Guyon, Y. Lecun, E. Sackinger, R. Shah, "Signature Verification using a "Siamese" Time Delay Neural Network," International Journal of Pattern Recognition and Artificial Intelligence, Vol.7, No.4(1994).
3 Han, H., S. Choi, "An Artificial Neural Network Approach for the Prediction of Unlawful Company in Defense Procurement," Journal of the Military Operations Research Society of Korea, Vol.37, No.1(2011).
4 Hochreiter, S., J. Schmidhuber, "Long Short-Term Memory," Neural Computation, Vol.9, No.8(1997).
5 Kim, M., H. Han, S. Choi, "A Study on the EAC Estimation of Defense Acquisition Project using Artificial Neural Network," Journal of Korea Management Engineers Society, Vol. 16, No.3(2011).
6 Kim, M. T., Y. T. Oh, W. J. Kim, "Sentence Similarity Prediction based on Siamese CNN-Bidirectional LSTM with Self-attention," Journal of KIISE, Vol.46, No.3(2019).
7 Kim, S. Y., Theory and Practice of Defense Acquisition, Bookorea, 2017.
8 Lee, D. K., M. T. Kim, W. J. Kim, "Query-based Answer Extraction using Korean Dependency Parsing," Journal of Intelligence and Information Systems, Vol.25, No.3(2019).
9 Lee, M. S., S. W. Yang, H. J. Lee, "Selective Word Embedding for Sentence Classification by Considering Information Gain and Word Similarity," Journal of Intelligence and Information Systems, Vol.25, No.4(2019).   DOI
10 Lin, Z., M. Feng, C. N. Santos, M. Yu, B. Xiang, B. Zhou, Y. Bengio, "A Structured Selfattentive Sentence Embedding," ICLR(2017).
11 Mueller, J., A. Thyagarajan, "Siamese recurrent architectures for learning sentence similarity," AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence(2016).
12 Park, H. Y., K. J. Kim, "Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Model," Journal of Intelligence and Information Systems, Vol.25, No.4(2019).   DOI
13 Rumelhart, D. E., J. L. McClelland, Parallel Distributed Processing, A Bradford Book, Cambridge, 1986.
14 Schuster, M., K. K. Paliwal, "Bidirectional Recurrent Neural Networks," IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol.45, No.11(1997).   DOI
15 Zhu, W., T. Yao, J. Ni, B. Wei, Z. Lu, "Dependency-based Siamese long short-term memory network for learning sentence representations," PLoS One, Vol.13, No.3(2018).