![]() ![]() Keywords frequently asked question (FAQ) information retrieval (IR) mobile health (mHealth) mobile Health Information system question and answer (Q&A) system short message service (SMS)/text message The retrieval efficacy was measured and it was confirmed that there was a significant improvement in the results of the proposed algorithm when compared with similar retrieval algorithms. Results were evaluated using three metrics: average precision, recall and computational time. The developed algorithm was used to retrieve the best-ranked question–answer pair. However, automating the short message services-based information search and retrieval poses significant challenges because of the inherent noise in its communications. This paper focuses on the design and development of a short message services-based information access algorithm to carefully screen information on human immunodeficiency virus/acquired immune deficiency syndrome within the context of a frequently asked questions system. Short message services, a much used function of cell phones, for example, can be turned into a major tool for accessing databases. Mobile phones have been identified as one of the technologies that can be used to overcome the challenges of information dissemination regarding serious diseases. Result from this study reveals the potential research issues, namely morphology analysis, question classification, and term weighting algorithm for question classification Based on this issues, the objective of this study is to review the current state of question analysis, document processing, and answer extraction techniques. ![]() The performance of all modules that has not been optimized has led to the less accurate answer from question answering systems. Challenge to optimize Question Answering framework is to increase the performance of all modules in the framework. Furthermore, answer extraction module receives the set of passages from document processing module, then determine the best answers to user. ![]() Document processing is a technique for identifying candidate documents, containing answer relevant to the user query. Question Analysis module has task to translate query into a form that can be processed by document processing module. This system consists of question analysis, document processing, and answer extraction module. Question Answering System could automatically provide an answer to a question posed by human in natural languages. We have got 96.22% accurate answer by using cosine similarity and 84.64% by Jaccard similarity in our proposed BIIB. For verifying our proposed systems, we have created 2852 questions from the introduced topics. We have also developed Bengali root word‘s corpus, synonym word‘s corpus, stop word‘s corpus, and collected 74 topic related questions and answers from the information of NSTU which are actually our inserted informative questions. In this study, we introduce a Bengali Language Toolkit (BLTK) and Bengali Language Expression (BRE) that make the easiest implementation of our task. For the action of chatbot in replying questions, we have applied the TF-IDF, cosine similarity and Jaccard similarity to find out the accurate answer from the documents. TF-IDF (Term Frequency-Inverse Document Frequency) has been used to convert character and/or string terms into numerical values, and to find their sentiments. In order to reduce the time complexity of searching questions and reply from inserted information, we have used Non-negative Matrix Factorization (NMF) as the topic modeling technique, and the Singular Value Decomposition (SVD) as to reduce the dimension of questions. ![]() We present the Bengali Anaphora Resolution system using the Hobbs‘ algorithm to get the correct expression of consequence questions. In the preprocessing part, this book is demonstrated by two algorithms for finding out the lemmatization of Bengali words such as Trie and Dictionary Based Search by Removing Affix (DBSRA) as well as compared with Edit Distance for the exact lemmatization. In this book, we introduce two mathematical and statistical procedures for BIIB based on information of Noakhali Science and Technology University (NSTU) that is significant mathematically and statistically. The Bengali Informative Intelligence Bot (BIIB) is an effective Machine Learning (ML) technique that helps a user to trace relevant information by Bengali Natural Language Processing (BNLP). ![]()
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