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

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   An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network

   Anran Jiao · 2020 · Journal of Physics, Conference Series · cited 73 times · Not summarized yet

   Abstract

   Intelligent chatbot systems are popular issues in the application fields of robot system and natural language processing. As the development of natural language processing and neural network algorithms, the application of artificial intelligence is increasing in Chatbot systems, which are typically used in dialog systems for various practical purposes including customer service or information acquisition. This paper designs the functional framework and introduces the principle of RASA NLU for the Chatbot system, then it integrates RASA NLU and neural network (NN) methods and implements the system based on entity extraction after intent recognition. With the experimental comparison and valida…

2. Very close Not relevant

   Chatbot: A Deep Neural Network Based Human to Machine Conversation Model

   G. Vamsi, A. Rasool, Gaurav Hajela · 2020 · International Conference on Computing Communication and Networking Technologies · cited 33 times · Not summarized yet

   Abstract

   A conversational agent (chatbot) is computer software capable of communicating with humans using natural language processing. The crucial part of building any chatbot is the development of conversation. Despite many developments in Natural Language Processing (NLP) and Artificial Intelligence (AI), creating a good chatbot model remains a significant challenge in this field even today. A conversational bot can be used for countless errands. In general, they need to understand the user's intent and deliver appropriate replies. This is a software program of a conversational interface that allows a user to converse in the same manner one would address a human. Hence, these are used in almost eve…

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   Deep Learning Based Chatbot Models

   Richard Csaky · 2019 · Computation and Language · preprint, not peer reviewed · cited 53 times · AI summary available

   Abstract

   A conversational agent (chatbot) is a piece of software that is able to communicate with humans using natural language. Modeling conversation is an important task in natural language processing and artificial intelligence. While chatbots can be used for various tasks, in general they have to understand users' utterances and provide responses that are relevant to the problem at hand. In my work, I conduct an in-depth survey of recent literature, examining over 70 publications related to chatbots published in the last 3 years. Then, I proceed to make the argument that the very nature of the general conversation domain demands approaches that are different from current state-of-of-the-art archi…

4. Very close Not relevant

   Chatbot using NLP

   Amutha R, M. Guru, Mohammed Nabeel Ahmed et al. · 2022 · Advances in Intelligent Systems and Technologies · Not summarized yet

   Abstract

   A chatbot is a application which is mainly used to provide text-based or text-to-speech online chat conversations in place of live human chat operators. The chatbot aims to establish connection between and a machine and a human. The machine incorporates data so that it can recognize the sentences and come to a conclusion in response to an inquiry. The response principle is the matching of the user's input phrase. The current technological effort involves establishing a chatbot-based system for college inquiries that uses Natural Language Processing to send natural language to a server. In this project, we're creating a chatbot that can answer users' questions about the college. The user will…

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   COVID-19 ChatBot

   Satish Tirumalapudi · 2021 · International Journal for Research in Applied Science and Engineering Technology · cited 1 time · Not summarized yet

   Abstract

   Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and resp…

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   AI-Based Chatbot System for Automated Customer Support Using NLP and Machine Learning

   Yash Desai, Lakshmi Jvn · 2026 · International journal of research and scientific innovation · Not summarized yet

   Abstract

   This study presents an intelligent chatbot system developed to automate customer support services in digital platforms. As user expectations for instant responses continue to grow, traditional support mechanisms face limitations in scalability and availability. The proposed system combines Natural Language Processing and Machine Learning techniques to interpret user queries and generate meaningful responses. A DistilBERT-based model is applied for intent detection, while a Named Entity Recognition module identifies relevant information within user input. The system was trained using conversational datasets and tested through multiple evaluation measures. The results demonstrate strong perfor…

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   The applications of chatbot

   Zhuo-xing Han · 2023 · Highlights in Science Engineering and Technology · cited 3 times · Not summarized yet

   Abstract

   A chatbot is a program that uses natural language to simulate a human conversation. and can receive questions from users to answer, as well as engage in casual conversation with users in a set format or help them complete a matter. It can free up personnel in an organization that is needed for reasonably stable, repetitive, and demanding tasks or procedures. Natural language processing (NLP), an essential aspect of artificial intelligence, forms the foundation of the system., and neural networks (NNs) that receive natural language and process responses. In recent years, chatbots have grown significantly with the rise of artificial intelligence, with achievements by major Internet companies, …

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   Automatic knowledge extraction of any Chatbot from conversation

   Sasa Arsovski, Hasmik Osipyan, Muniru Idris Oladele et al. · 2019 · Expert systems with applications · cited 30 times · Not summarized yet

   Abstract

   Abstract Acquiring knowledge for conversation modeling is an important task in the process of building a Conversational Agent (Chatbot). However, it is a quite difficult process that requires too much time and efforts. To overcome these difficulties, in this paper, we demonstrate a novel methodology for the automatic conversational knowledge extraction from an existing Chatbot. Extracted knowledge will be used as training dataset for building a Neural Network Conversational Agent. The experiments in the paper show that our proposed approach can be used for the automatic knowledge extraction from any type of Chatbot on the Internet. The experiment that is presented in this paper has two phase…

Prior work it builds on

1. Close A method it uses Not relevant

   A Neural Conversational Model

   O. Vinyals, Quoc V. Le · 2015 · arXiv.org · preprint, not peer reviewed · cited 1,843 times · Not summarized yet

   Abstract

   Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversation…

2. Close A method it uses Not relevant

   Named Entity Recognition with Bidirectional LSTM-CNNs

   Jason P. C. Chiu, Eric Nichols · 2015 · Transactions of the Association for Computational Linguistics · cited 2,013 times · Not summarized yet

   Abstract

   Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpas…

3. Close A method it uses Not relevant

   Neural Responding Machine for Short-Text Conversation

   Li-Feng Shang, Zheng-Dong Lu, Hang Li · 2015 · Annual Meeting of the Association for Computational Linguistics · cited 1,163 times · Not summarized yet

   Abstract

   We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large amount of one-round conversation data collected from a microblogging service. Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-ba…

4. Close Not relevant

   Evaluating Natural Language Understanding Services for Conversational Question Answering Systems

   Daniel Braun, Adrian Hernandez-Mendez, F. Matthes et al. · 2017 · SIGDIAL Conference · cited 195 times · Not summarized yet

   Abstract

   Conversational interfaces recently gained a lot of attention. One of the reasons for the current hype is the fact that chatbots (one particularly popular form of conversational interfaces) nowadays can be created without any programming knowledge, thanks to different toolkits and so-called Natural Language Understanding (NLU) services. While these NLU services are already widely used in both, industry and science, so far, they have not been analysed systematically. In this paper, we present a method to evaluate the classification performance of NLU services. Moreover, we present two new corpora, one consisting of annotated questions and one consisting of annotated questions with the correspo…

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   Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems

   Abdallah Bashir, Abubakr Hassan, Benjamin Rosman et al. · 2018 · International Conference on Arabic Computational Linguistics · cited 17 times · Not summarized yet

   Abstract

   Abstract Natural Language Understanding (NLU) is considered a core component in implementing dialogue systems. NLU has been greatly enhanced by deep learning techniques such as word embeddings and deep neural network architectures, but current NLP methods for Arabic language dialogue action classification or semantic decoding is mostly based on handcrafted rule-based systems and methods that use feature engineering, but without the benefit of any form of distributed representation of words. This paper presents an approach to use deep learning techniques for text classification and Named Entity Recognition for the domain of home automation in Arabic. To this end, we present an NLU module that…

Papers that cite it

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   Multi-task Learning for Named Entity Recognition and Intent Classification in Natural Language Understanding Applications

   Rizal Setya Perdana, P. P. Adikara · 2025 · Journal of Information Systems Engineering and Business Intelligence · cited 11 times · Not summarized yet

   Abstract

   Background: Understanding human language is a part of the research in Natural Language Processing (NLP) known as Natural Language Understanding (NLU). It becomes a crucial part of some NLP applications such as chatbots, that interpret the user intent and important entities. NLU systems depend on intent classification and named entity recognition (NER) which is crucial for understanding the user input to extract meaningful information. Not only important in chatbots, NLU also provides a pivotal function in other applications for efficient and precise text understanding. Objective: The aim of this study is to introduce multitask learning techniques to improve the application's performance on N…

2. Close Uses this paper in depth Not relevant

   Conversational agent in HCI a review

   Yi-Xuan Zhao, Tianbo Lin, Yi-Ze Wang · 2024 · Applied and Computational Engineering · cited 1 time · Not summarized yet

   Abstract

   Nowadays, AI technology is developing rapidly and slowly covering all areas of life. AI facilitates different parts of our lives and provides us with a lot of help. For example, in learning, students can acquire knowledge more conveniently through AI, and buyers can find suitable goods more conveniently through AI when shopping. At the same time, more and more technology is used in the field of voice agents, which allows humans to enjoy a lot of better services. In this article, we will study to better understand "how to understand human natural language and how the repository of knowledge is built." In the article we build with examples and deep learning models (CNN and RNN) through databas…

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   A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language

   Khadija Akter Lima, Khan Md Hasib, Sami Azam et al. · 2023 · PLoS ONE · cited 15 times · Not summarized yet

   Abstract

   Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art perform…

4. Related Not relevant

   Integrating RASA Chatbot with an Intent Layer for SDN-Based Network Slice Provisioning

   M. Gharbaoui, P. Castoldi · 2024 · International Conference on Network of the Future · cited 7 times · Not summarized yet

   Abstract

   The increasing complexity of modern networking infrastructures demands innovative solutions for efficient provisioning and management of network resources. In this paper, we present a RASA-based approach, an open-source conversational AI platform, to develop a chatbot interface for interacting with an intent layer to request Software-Defined Networking (SDN)-based network slices. Our proposed system aims to simplify the process of provisioning network slices by enabling users to express their intent in natural language, abstracting away the complexities of the underlying infrastructure. The chatbot interprets user requests, extracts intents and entities using natural language understanding t…

5. Related Not relevant

   AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

   Sheetal Kusal, S. Patil, J. Choudrie et al. · 2022 · IEEE Access · cited 116 times · Not summarized yet

   Abstract

   Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement convers…