KNN vs. DNN: auto chatbot

Qiushi Xu
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Abstract

Machine learning has played a very important role these days. A very common area related to daily life is auto chatbots, such as Apple Siri, Google Home, Mi Xiaoai, and others. These technologies all use deep learning as the foundation to achieve the goal of communicating with humans. Engineers and computer scientists put a massive amount of effort into trying to improve the quality of chatbots by improving the models that drive this feature. From previous research, KNN (K-nearest neighbors) and DNN (Deep Neural Network) are two widely used models in the machine learning area. To find out which is more efficient to manage auto-chat while understanding deeper how chatbots actually manage to recognize human language and quickly come up with corresponding answers, the two learning models were applied to a self-made chatbot. By comparing the effectiveness of applying K-nearest neighbors and Deep Neural Network, the paper finds that KNN runs faster and takes less space since it is a comparatively simpler algorithm. In terms of correctness, in this experiment, both algorithms turned out to have a similar percentage of correctness. This might be caused by a comparatively small dataset.
KNN与DNN:自动聊天机器人
如今,机器学习扮演着非常重要的角色。与日常生活相关的一个非常常见的领域是自动聊天机器人,比如苹果Siri、谷歌Home、小米小爱等。这些技术都以深度学习为基础,实现与人类交流的目标。工程师和计算机科学家投入了大量的精力,试图通过改进驱动这一功能的模型来提高聊天机器人的质量。从之前的研究来看,KNN (k近邻)和DNN(深度神经网络)是机器学习领域中被广泛使用的两个模型。为了找出哪一种更有效地管理自动聊天,同时更深入地了解聊天机器人是如何识别人类语言并快速给出相应答案的,我们将这两种学习模型应用于一个自制聊天机器人。通过比较应用k近邻和Deep Neural Network的有效性,本文发现KNN算法相对简单,运行速度更快,占用空间更少。在正确性方面,在这个实验中,两种算法的正确性百分比是相似的。这可能是由于数据集相对较小造成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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