NLP for Chatbot Application

Nithyanandam, S.; Kasinathan, Sharmila; Radhakrishnan, Devi; Jebapandian, Jebathangam

doi:10.4018/978-1-7998-7728-8.ch008

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The term "Term Frequency"(TF) is used to count how many times a time appears in a document [33]. There are 5000 words in document "T1," and the word "alpha" appears ten times.…”

Section: Big Mouth: Using Tf-idf Vectorizationmentioning

confidence: 99%

Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis

Attigeri,

Agrawal,

Kolekar

2024

IEEE Access

View full text Add to dashboard Cite

The process to obtain the information about university/institute is a critical stage in the academic journey of prospective students who are seeking information about the specific courses which makes that university/institute unique. This process begins with exploration to general information about universities through websites, rankings, and brochures from various sources. Most of the time, information available on different sources leads to discrepancies and influences student's decisions. By addressing inquiries promptly and providing valuable information, universities can guide individuals in making informed choices about their academic future. To address this, the chatbot application is the most effective tool to be implemented and make it functional on university's functional website. A chatbot is an artificially intelligent tool which can interact with humans and can mimic a conversation. This tool can be implemented using advanced Natural Language Processing (NLP) models to provide the pre-defined answers to the student's queries. Chat-bot is very helpful for query resolution during the counseling process of the institute as it will provide official/uniform information and can be accessed 24x7. Therefore, the aim of this research work was to implement a chat-bot using various NLP models and compare them to identify best one. In this work, five chat-bot models were implemented using neural networks, TF-IDF vectorization, sequential modeling and pattern matching. From the results, it was observed that neural network-related models had better accuracy than TF-IDF and pattern matching model, and sequential modeling is the most accurate model because it prevents over-fitting. Furthermore, a chat-bot having any kind of optimizer can improve the result and it is most important that pattern matching, and semantic analysis should be the parts of a chat-bot for real time scenarios.

show abstract

“…The term "Term Frequency"(TF) is used to count how many times a time appears in a document [33]. There are 5000 words in document "T1," and the word "alpha" appears ten times.…”

Section: Big Mouth: Using Tf-idf Vectorizationmentioning

confidence: 99%

Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis

Attigeri,

Agrawal,

Kolekar

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Through this process, the model acquires the structure of the language, including grammar, semantics, and some context. This helps the model to understand the nuances of language and build a basic and comprehensive understanding of language [24,25]. The architecture of the novel hybrid model is based on the transformer framework.…”

Section: Modeling Backgroundmentioning

confidence: 99%

“…True positives True positives + False positives (25) This formula quantifies the accuracy of the model's predictions concerning positive instances.…”

Section: Precision =mentioning

confidence: 99%

Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model

Bratić,

Šapina,

Jurečić

et al. 2024

ASI

View full text Add to dashboard Cite

This paper addresses the challenges associated with the centralized storage of educational materials in the context of a fragmented and disparate database. In response to the increasing demands of modern education, efficient and accessible retrieval of materials for educators and students is essential. This paper presents a hybrid model based on the transformer framework and utilizing an API for an existing large language model (LLM)/chatbot. This integration ensures precise responses drawn from a comprehensive educational materials database. The model architecture uses mathematically defined algorithms for precise functions that enable deep text processing through advanced word embedding methods. This approach improves accuracy in natural language processing and ensures both high efficiency and adaptability. Therefore, this paper not only provides a technical solution to a prevalent problem but also highlights the potential for the continued development and integration of emerging technologies in education. The aim is to create a more efficient, transparent, and accessible educational environment. The importance of this research lies in its ability to streamline material access, benefiting the global scientific community and contributing to the continuous advancement of educational technology.

show abstract

Design and Development of Conversational Chatbot for Covid-19 using NLP: an AI application

Singh

Kaur

Tanwar

et al. 2022

2022 6th International Conference on Computing Methodologies and Communication (ICCMC)

View full text Add to dashboard Cite

NLP for Chatbot Application

Cited by 5 publications

References 6 publications

Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis

Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis

Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model

Design and Development of Conversational Chatbot for Covid-19 using NLP: an AI application

Contact Info

Product

Resources

About