WSD Team's Approaches for Textual Entailment Recognition at the NTCIR10 (RITE2)

In this paper, we describe the WSD team’s approaches to textual entailment recognition task (RITE) at NTCIR-10[1], a conference held in June 18-21, 2013, at NII in Tokyo, Japan, and present experimental results for three Japanese subtasks, called “Binary Class” (BC), “Multi Class” (MC) and “Entrance Exam BC” (ExamBC). Our approach employs two supervised learning techniques: support vector machine (SVM) and logistic regression (LR). For the binary classification subtasks (BC and ExamBC), we propose hand-coded rules to classify text into entailment or non-entailment categories, while for the multi-classification subtask (MC), we used the bidirectional features for the texts. The best performance in three runs achieved precision of 80.66% in BC subtask, 69.53% in MC subtask and 67.86% in ExamBC subtask. We won second place for BC and MC, and third place for ExamBC.