Using minimal recursion semantics in Japanese question answering

要旨

Question answering is a research field with the aim of providing answers to a user’s question, phrased in natural language. In this thesis I explore some techniques used in question answering, working towards the twin goals of using deep linguistic knowledge robustly as well as using language-independent methods wherever possible. While the ultimate aim is cross-language question answering, in this research experiments are conducted over Japanese data, concentrating on factoid questions. The two main focus areas, identified as the two tasks most likely to benefit from linguistic knowledge, are question classification and answer extraction. In question classification, I investigate the issues involved in the two common methods used for this task—pattern matching and machine learning. I find that even with a small amount of training data (2000 questions), machine learning achieves better classification accuracy than pattern matching with much less effort. The other issue I explore in question classification is the classification accuracy possible with named entity taxonomies of different sizes and shapes. Results demonstrate that,

主題