Minimally Supervised Question Classification and Answering based on WordNet and Wikipedia

Minimally Supervised Question Classification and Answering based on WordNet and Wikipedia

在此篇論文中，我們提出一個自動將問題分類至現有詞網（WordNet）中之細分類方法。為此，我們利用維基百科之特性以及其文章標題，建立大規模語意實體分類表，包含了1,581,865個實體。為了表現我們研究之效用，我們建構了一個基於冗餘原則的自動問題回答系統，並透過所提出的問題分類方法來增進其效能。實驗結果顯示所提出的方法能夠有效地提升問題分類與回答的精確率。

In this paper, we introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, we also constructed a large scale entity supersense database that contains over 1.5 million entities to the 25 WordNet lexicographer's files (supersenses) from titles of Wikipedia entry. To show the usefulness of our work, we implement a simple redundancy-based system that takes the advantage of the large scale semantic database to perform question classification and named entity classification for open domain question answering. Experimental results show that the proposed method outperform the baseline of not using question classification.