( 您好!臺灣時間:2023/12/11 19:30
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


論文名稱(外文):A Study and Development of a Smart Chatbot Agent Based on Civil Law of Taiwan
外文關鍵詞:ChatbotLINECivil LawAIMLRiveScript
  • 被引用被引用:3
  • 點閱點閱:396
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
隨著人工智慧的快速發展,各種相關科技應用讓我們的生活更為便利,例如:藉由人工智慧開發出聊天機器人,能夠立即提供客戶各種解答或是解決方案。有鑑於此,本論文結合人工智慧與深度學習中的自然語言處理(Natural Language Processing, NLP)以及序列對序列(sequence-to-sequence, Seq2seq)模型,開發一套具有深入學習的聊天室機器人讓使用者的生活更加便利。因此,我們想要開發出一款台灣民法聊天機器人(Civil Law of Taiwan, CLT)提供大家可以查詢相關台灣民法的聊天機器人,讓民眾可以對民法法律多一個自助諮詢的管道。
CLT系統植於LINE的平台上,本系統以AIML或是RiveScript結合LINE API與台灣民法搭配,利用模糊比對來判斷使用者詢問的問題是屬於哪一個單元,再來進入到民法AI引擎找出最符合的答案,透過LINE回傳給使用者。CLT系統的完成,可以讓每個使用者都可以透過即時的對話式服務來解決對民法的基本常見問題,並無時無刻都能增進自己民法的知識。

Interactions between individuals inevitably produce friction that reconciliatory methods may not always effectively resolve. The ultimate course of action is to enter into legal proceedings. However, the general public does not share the same familiarity and expertise as legal professionals in navigating the arcane and complicated legal system. They may seek the assistance of attorneys of law, but that would create additional cost.
The rapid development of artificial intelligence (AI) and associated technological applications have brought considerable convenience to our everyday lives. For instance, AI-enabled conversational robots can provide clients answers or solutions to a range of the problem. With these developments in mind, this paper combined AI, Natural Language Processing (NLP), and sequence-to-sequence (Seq2seq), which are models under deep learning, to develop a chatroom robot with deep learning capability that can improve user experience as they adopt a more convenient lifestyle. We wish to develop a chat robot specializing in the Civil Law of Taiwan (CLT) so to provide the general public with an additional channel for consultation. The public can use the robot to conduct inquiries regarding Civil Law themselves.
The CLT system is based on the Line-app platform. The system will use AIML or RiveScript to pair the Line API and the Taiwan Civil Law corpus to perform approximate matching. The method can determine the unit or category that the user's inquiry belongs to, then import the query into the Civil Law-dedicated AI search engine to locate the appropriate results. The results are then sent to the user via the Line app. The completion of the CLT system can provide each user with instantaneous conversational service to resolve basic and common problems that the user may have regarding Civil Law, leading to improved legal literacy.

致謝 ii
摘要 iii
目錄 v
表次 vi
圖次 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第二章 文獻探討 3
第一節 深度學習 3
第二節 聊天機器人 10
第三節 AIML、RiveScript 18
第四節 民法 21
第三章 系統架構 23
第一節 系統概念與架構 23
第二節 民法AI引擎 27
第三節 LINE聊天機器人架構 28
第四章 實驗結果 31
第一節 LINE聊天機器人串接 31
第二節 LINE聊天機器人設計 31
第三節 執行成果 41
第五章 結論與未來工作 45
第一節 結論 45
第二節 未來工作 45
參考文獻 47




Aust, H., Oerder, M., Seide, F., & Steinbiss, V. (1995). The Philips automatic train timetable information system. Speech Communication, 17(3-4), 249-262.

Bellegarda, J. R. (2013). Large-scale personal assistant technology deployment: the siri experience. In INTERSPEECH (pp. 2029-2033).

N. Bush, “The predictive value of transitional probability for word-boundary palatalization in English,” Unpublished M.S .thesis, Univ. New Mexico, Albuquerque, NM, 1999.

A. Berger, V. D. Pietra, and S. D. Pietra, “A maximum entropy approach to natural language processing,” Comput. Linguistics, vol. 22, no. 1, pp. 39–71, 1996.

Cahn, J. (2017). CHATBOT: Architecture, design, and development. University of Pennsylvania School of Engineering and Applied Science Department of Computer and Information Science.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Chouard, T. (2016). The Go Files: AI computer wraps up 4-1 victory against human champion. Nature News.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Colby, K. M., Weber, S., & Hilf, F. D. (1971). Artificial paranoia. Artificial Intelligence, 2(1), 1-25.

M. Dyer, “Connectionist natural language processing: A status report,” in Computational Architectures Integrating Neural and Symbolic Processes, R. Sun and L. Bookman, Eds. Dordrecht, The Netherlands: Kluwer Academic, 1995, vol. 292, pp. 389–429

Ginzburg, J., & Fernández, R. (2010). Computational models of dialogue. Handbook of Computational Linguistics and Natural Language, Oxford. Blackwell, 429-481.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Jurafsky, D., & Martin, J. H. (2014). Speech and language processing (Vol. 3). London:: Pearson.

T. Joachims, Learning To Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Norwell, MA: Kluwer Academic, 2002.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.

J. Lafferty, A. McCallum, and F. Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in Proc. 18th Int. Conf. Machine Learning, 2001, pp. 282–289.

Manning, C. D. & Schutze, H. (1999) Foundations of Statistical Natural Language Processing (MIT Press, Cambridge, MA).

McTear, M. F. (2002). Spoken dialogue technology: enabling the conversational user interface. ACM Computing Surveys (CSUR), 34(1), 90-169.

Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, “Text classification from labeled and unlabeled documents using EM,” Machine Learn., vol. 39, nos. 2–3, pp. 103–134, 2000.

Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 311-318). Association for Computational Linguistics.

Pascanu, R., Mikolov, T., & Bengio, Y. (2012). Understanding the exploding gradient problem. CoRR, abs/1211.5063.

Reiter, E., & Dale, R. (2000). Building natural language generation systems. Cambridge university press.

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. nature, 323(6088), 533.

Schalkoff, R. J. (1997). Artificial neural networks (Vol. 1). New York: McGraw-Hill.

Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016). Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In AAAI (Vol. 16, pp. 3776-3784).

Seymore, K., McCallum, A., & Rosenfeld, R. (1999). Learning hidden Markov model structure for information extraction. In AAAI-99 workshop on machine learning for information extraction (pp. 37-42).

Shawar, B. A., & Atwell, E. (2007). Chatbots: are they really useful?. In Ldv forum (Vol. 22, No. 1, pp. 29-49).

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104-3112).

Turing, A. M. (1950). Computing Machinery And Intelligence. Mind, LIX(236), 433–460.

Wallace, R. S. (2009). The anatomy of ALICE. In Parsing the Turing Test (pp. 181-210). Springer, Dordrecht.

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.

Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2), 270-280.

Young, S. J. (2000). Probabilistic methods in spoken–dialogue systems. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 358(1769), 1389-1402.


吳惠麟(2016),網管系統結合 LINE BOT 實現即時告警互動通知,取自於2017年8月14日,於http://www.netadmin.com.tw/article_content.aspx?sn=1610040003&jump=2。



Andrej Karpathy. (2015). The Unreasonable Effectiveness of Recurrent Neural Networks. Retrieved May 6, 2018, from http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Carpenter, R. (2017). Cleverbot. Retrieved May 11, 2018 , from http://www.cleverbot.com/

Christopher Olah. (2015). Understanding LSTM Networks. Retrieved May 27, 2018, from

Diditho. (2014). Technology Stack (Server) ‘Kompas TTS’. Retrieved October 5, 2017, from http://diditho.com/2014/12/10/technology-stack-aplikasi-kompas

Hugo Férée. (2011). Turing Test Version 3.svg. Retrieved May 20, 2018, from https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence

LINE Corporation. (2017). LINE Developers. Retrieved May 16, 2017, from https://developers.Line.me/bot-api/overview

Microsoft. (2018). Xiaoice. Retrieved May 12, 2018, from https://poem.msxiaobing.com/

Radim Řehůřek. (2017). Gensim. Retrieved March 11, 2018, from https://radimrehurek.com/gensim/models/word2vec.html

Richard Wallace. (2011). AIML, Retrieved October 30, 2011, from https://en.wikipedia.org/wiki/AIML?fbclid=IwAR1e7lfs4B1Q6aRwnTOLOeWwTq34biSECnjNkaGNvJiRYKVdjLL55tIzAVc

Sun Junyi. (2017). Jieba. Retrieved May 20, 2017, from https://github.com/fxsjy/jieba

Thang Luong, Eugene Brevdo, Rui Zhao. (2018). Tensorflow.
Retrieved March 11, 2018, from https://www.tensorflow.org/tutorials/seq2seq

電子全文 電子全文(網際網路公開日期:20240829)
第一頁 上一頁 下一頁 最後一頁 top