We present a Korean question answering framework for restricted domains, called K-QARD. K-QARD is developed to achieve domain portability and robustness, and the framework is successfully applied to build question answering systems for several domains.

This paper proposes a probabilistic model for definitional question answering (QA) that reflects the characteristics of the definitional question. The intention of the definitional question is to request the definition about the question target. Therefore, an answer for the definitional question should contain the content relevant to the topic of the target, and have a representation form of the definition style. Modeling the problem of definitional QA from both the topic and definition viewpoints, the proposed probabilistic model converts the task of answering the definitional questions into that of estimating the three language models: topic language model, definition language model, and general language model. The proposed model systematically combines several evidences in a probabilistic framework. Experimental results show that a definitional QA system based on the proposed probabilistic model is comparable to state-of-the-art systems.

While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our proposed naive Bayes text classifier performs very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM.