| Resumen |
The article presents the experiments carried out as part of the participation in Recognizing Inference in Text (NTCIR-9 RITE) @NTCIR9 for Japanese.NTCIR-9 RITE has four subtasks, Binary-class (BC) subtask, Multi-class (MC) subtask, Entrance Exam and NTCIR-9 RITE4QA. We have submitted a total of three unique runs (Run 1, Run 2 and Run 3) in the BC subtask and one run each in the MC Subtask, Entrance Exam subtask and NTCIR-9 RITE4QA subtask. The first system for BC subtask is based on Machine Translation using the web based Bing translator system. The second system for the BC subtask is based on lexical matching. The third system is based on a voting approach on the outcomes of the first and the second system. The system for MC subtask is based on a learned system that uses different lexical similarity features like Word Net based Unigram Matching, Bigram Matching, Trigram Machine, Skip-gram Matching, LCS Matching and Named Entity (NE) Matching. For Entrance Exam and NTCIR-9 RITE4QA subtask, we develop a single system based on the Ngram matching module similar to the second system of the BC subtask. For the BC subtask, the accuracy for Run 1, Run 2 and Run 3 are 0.490, 0.500 and 0.508 respectively. For the MC subtask, the accuracy is 0.175. The accuracy figures of the Entrance Exam subtask and the NTCIR-9 RITE4QA subtask are 0.5204 and 0.5954 respectively. |
