Background: In the era of information overload it is very difficult for a human reader to
make sense of the vast information available on the internet quickly. Even for a specific domain like
a college or university website, it may be difficult for a user to browse through all the links to quickly
get the relevant answers.
Objective: In this scenario, the design of a chat-bot which can answer questions related to college
information and compare between colleges will be very useful and novel.
Methods: In this paper, a novel conversational interface chat-bot application with information retrieval
and text summarization skill is designed and implemented. Firstly, this chat-bot has a simple
dialog skill; when it can understand the user query intent, it responds from the stored collection of
answers. Secondly, for unknown queries, this chat-bot can search the internet, and then perform text
summarization using advanced techniques of natural language processing (NLP) and text mining
Results: The advancement of NLP capability of information retrieval and text summarization using
machine learning techniques of Latent Semantic Analysis (LSI), Latent Dirichlet Allocation (LDA),
Word2Vec, Global Vector (GloVe) and TextRank is reviewed and compared in this paper first before
implementing them for the chat-bot design. This chat-bot improves user experience tremendously
by getting answers to specific queries concisely which takes less time than to read the entire
document. Students, parents and faculty can get the answers for a variety of information like admission
criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile,
research papers, patents, etc. more efficiently.
Conclusion: The purpose of this paper was to follow the advancement in NLP technologies and implement
them in a novel application.