文字蘊涵是自然語言處理最近興起的研究課題。文字蘊涵識別(Recognizing Textual Entailment, RTE)可以應用到其他許多自然語言處理的研究中。在本文中將介紹我們在觀察NTCIR-10-RITE-2資料集後發現過去系統的缺陷，進而提出如何改進中文文字蘊涵系統的方法。過去的系統處理文字蘊涵多使用機器 學習分類文題的方法，所有輸入句子都用同樣的分類器處理，對於某些特別的問題往往會產生誤判。我們認為應該針對於特定類型的問題做處理，増加系統可以處理的問題類型。實驗結果顯示配合之前提出的機器學習方法，増加四種特殊類型分類對特殊類型句子進行個別處理，可以有效改進系統，實驗結果系統在識別簡體中文蘊涵兩類的正確率從原本67.86%提昇到72.92%。

Recognizing Textual Entailment (RTE) is a new research issue in natural language processing (NLP) research area. RTE can be a useful component in many NLP applications. In this paper, we introduce our finding on the entailment analysis of the NTCIR-10 RITE-2 dataset, and use the observation to improve our system. In the previous works, all the input pairs are treated equally in a standard classification architecture. We find that is not suitable for some special cases. We believe that by isolating the special cases and building separated classifiers, a RTE system can perform better. After implementing modules for four special cases into our system, the result is significantly improved from 67.86% to 72.92% on the binary class classification task.