A Model of Vietnamese Person Named Entity Question Answering System
Published in PACLIC, Bali, Indonesia, 2012
In this paper, we proposed a Vietnamese named entity question answering (QA) model. This model applies an analytical question method using CRF machine learning algorithm combined with two automatic answering strategies: indexed sentences database-based and Google search engine-based. We gathered a Vietnamese question dataset containing about 2000 popular “Who, Whom, Whose” questions to evaluate our question chunking method and QA model. According to experiments, question chunking phase acquired the average F1 score of 92.99%. Equally significant, in our QA evaluation, experimental results illustrated that our approaches were completely reasonable and realistic with 74.63% precision and 87.9% ability to give the answers.
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