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automatic frequently asked question generation

In conventional Question Answering systems, customers are allowed to express their queries in a natural language format. We propose to extend the existing work on question answering systems to generating automated Frequently Asked Question (FAQ) lists. The primary research goal is to go beyond the functionality of traditional FAQ retrieval systems. Our proposed system accepts, extracts, or generates user questions in order to create, maintain, and improve the FAQs quality. We are proposing an entire framework to model and formalize the interactions and interpretability between and among Question Answering system modules. In addition to present the basis of QA system, complimentary units will be added to conduct FAQ list. A new methodology is proposed to construct the questions by using a hybrid of question extraction and question generation methods. Our question extraction approach is motivated by a set of syntactic, semantic, and rule based analysis which extract interrogative sentences which are asked in the text. We will conduct a Question Generation process to enrich the question repository in order to achieve superior quality of nal FAQ list. Selection of documents that are considered for further analysis is critical step in the whole QA process specially in answer retrieval. Our approach here is to identify appropriate ways of choosing the documents from
various sources with respect to their likelihood of containing a high ranked answer. Advanced ranking paradigms will be used, to recover more relevant Question/Answer pairs for the nal FAQ list. The viability of the proposed system will be evaluated in twofold, component and system wise. The research proposed here will contribute to the eld of Natural Language Processing, Text Mining, Question Answering, particularly to provide high quality automatic FAQ generation and retrieval.

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