NiCT/ATR in NTCIR-7 CCLQA track: Answering complex cross-lingual questions
Youzheng Wu, Wenliang Chen, Hideki Kashioka · NTCIR · 2008
This paper describes our complex cross-lingual question answering (CCLQA) system for NTCIR-7 ACLIA track. To answer complex questions such as events, biographies, definitions, and relations, we designed two models, i.e., the centroid-vector model and the SVMbased model. In the official evaluation of the NTCIR7 CCLQA track, our SVM-based model achieved 22.11% F-score in the English-Chinese cross-lingual task, the highest score among all participants’ systems, and 23.16% F-score in the Chinese-Chinese monolingual task. In the automatic evaluation, the F-scores of the SVM-based model and the centroidvector model in the English-Chinese task are 27.24%, and 24.55%,respectively. In the Chinese-Chinese task, the two models achieved 28.30%, and 24.78% Fscores.