ARmed question answering system

Abstract

Question answering, retrieving an exact answer to a question posed in natural language, is an issue which has widely been studied in the open domain over the last decades. This, however, remains a real challenge in the medical domain as most existing systems only support a limited amount of question and answer types. The problem with proposed methods for Arabic language in the medical domain is that there is often a conflict between the extracted answer and user's requirements. This conflict is related to ambiguity. Nevertheless, the method we propose has successfully tackled this problem. Thus, in this article, we introduce ARmed, a system for automatically answering medical questions for Arabic language. ARmed consists of corpora study, pre-processing, question analysis, documents/passages retrieval, and answer extraction. Compared with the previous studies, ARmed has the potential to handle a large number of questions and answer types. The experimental results show that ARmed achieves interesting results.

An Efficient Matching Algorithm for Question Answering System

“…And many farmers don't know enough about the Internet, making it difficult for them to use the Internet to get the knowledge they need. The emergence of an intelligent question answering system can effectively solve this problem, users only need to enter the questions they want to query through voice or text [1], intelligent question answering system can efficiently screen out the most accurate answer for users, simplifying the process of users to obtain knowledge [2]. The existing intelligent question answering system for wheat and corn has problems such as slow retrieval speed and low retrieval efficiency, and this paper constructs an efficient and accurate two-stage matching algorithm to help users solve problems in the planting process [3].…”