Graph-based Document Expansion and Robust SCR Models for False Positives: Experiments at the NTCIR-12 SpokenQuery&Doc-2

In this paper, we report our experiments at NTCIR-12 Spoken Query&Doc-2 task. We participated spoken query driven spoken content retrieval (SQ-SCR) subtasks of Spoken Query&Doc2. We submited two types of results, which are conventional spoken content retrieval method (referred to as C-SCR) and STD based approach for SCR (referred to as STD-SCR). The latter was proposed in order to deal with speech recognition errors and out-of-vocabulary (OOV) words. We extend each SCR methods by several ways. For C-SCR, we applied graph-based document expansion method. For STD-SCR, we applied robust retrieval models for false positive errors by using word co-occurrences information.