本研究實驗結果顯示，利用我們系統所加入之語意特徵為基礎，並利用機器學習進行特徵的分類，使用特徵選取的方法得到最佳化的特徵組合，在NTCIR-10 RITE-2之中文文本蘊涵辨識的整體準確率在繁體BC子任務中達到73.28%，在簡體BC子任務中達到74.57% ，本研究的主要貢獻為，我們於實驗中加入語意特徵方法對中文文本蘊涵辨識之準確率有大幅提升之效果。 Recognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which addressing major text understanding in information access research areas.
In this paper, we proposed a Chinese textual entailment system using Wordnet semantic and dependency syntactic approaches in Recognizing Inference in Text (RITE) using the NTCIR-10 RITE-2 subtask datasets. Wordnet is used to recognize entailment at lexical level. Dependency syntactic approach is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis.
We thoroughly evaluate our approach using NTCIR-10 RITE-2 subtask datasets. As a result, our system achieved 73.28% on Traditional Chinese Binary-Class (BC) subtask and 74.57% on Simplified Chinese Binary-Class subtask with NTCIR-10 RITE-2 development datasets. Thorough experiments with the text fragments provided by the NTCIR-10 RITE-2 subtask showed that the proposed approach can improve system''s overall accuracy.
