The development of a question-answering system for learning article using natural language processing

This paper proposes developing a question-answering (QA) system for Thai learning articles. This system is a closed-domain QA system. Thai text processing was proposed to retrieve answers relevant to the natural language-based questions specified by users. The methodology to generate the keywords/topics used for retrieving answers was introduced. Matching and selecting methods for obtaining the best answer using cosine similarity is the support technique. Creating the corpus database, which contains Thai synset - a set of words with similar meanings or that may relate to each other, was explained. With the proposed corpus database, the question-answering can extract and analyze the meaning of the unbound sentence in Thai. The proposed QA system performed well, with a precision score of 87.67%, a recall score of 73.35%, and an F1 score of 81.91%.