论文标题
聊天机器人：使用人工神经网络使用命名实体识别模型的对话代理
Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network
论文作者
论文摘要
聊天机器人是一种用于使用自然语言模仿人类行为的技术。有不同类型的聊天机器人可以用作各种业务领域中的对话代理，以提高客户服务和满意度。对于任何业务领域，它都需要为该域构建知识库并设计基于信息检索的系统，该系统可以用文档或生成的句子响应用户。聊天机器人的核心组成部分是自然语言理解（NLU），它通过深度学习方法得到了令人印象深刻的改进。但是我们通常缺乏正确构建的NLU模块，需要更多时间从头开始建造它以进行高质量的对话。这可能会鼓励新鲜的学习者通过简单的体系结构从头开始构建聊天机器人，并使用小型数据集，尽管它可能降低了功能，而不是构建高质量数据驱动的方法。这项研究重点是命名实体识别（NER）和意图分类模型，这些模型可以集成到聊天机器人的NLU服务中。指定的实体将在知识库中手动插入，并在给定句子中自动检测到。拟议的体系结构中的NER模型基于人工神经网络，该网络对手动创建的实体进行了培训，并使用CONLL-2003数据集进行了评估。
Chatbot is a technology that is used to mimic human behavior using natural language. There are different types of Chatbot that can be used as conversational agent in various business domains in order to increase the customer service and satisfaction. For any business domain, it requires a knowledge base to be built for that domain and design an information retrieval based system that can respond the user with a piece of documentation or generated sentences. The core component of a Chatbot is Natural Language Understanding (NLU) which has been impressively improved by deep learning methods. But we often lack such properly built NLU modules and requires more time to build it from scratch for high quality conversations. This may encourage fresh learners to build a Chatbot from scratch with simple architecture and using small dataset, although it may have reduced functionality, rather than building high quality data driven methods. This research focuses on Named Entity Recognition (NER) and Intent Classification models which can be integrated into NLU service of a Chatbot. Named entities will be inserted manually in the knowledge base and automatically detected in a given sentence. The NER model in the proposed architecture is based on artificial neural network which is trained on manually created entities and evaluated using CoNLL-2003 dataset.
