Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network

• URL: http://arxiv.org/abs/2007.04248v1
• Date: Fri, 19 Jun 2020 14:47:21 GMT
• Title: Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network
• Authors: Nazakat Ali
• Abstract summary: Natural Language Understanding (NLU) has been impressively improved by deep learning methods. This research focuses on Named Entity Recognition (NER) models which can be integrated into NLU service of a dataset. The NER model in the proposed architecture is based on artificial neural network which is trained on manually created entities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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