Shan-Shun Yang

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In this paper, we describe how we improve our system for Chinese Textual Entailment Recognition by a monolingual machine translation system. Previously, our approach is based on the standard supervised learning classification. We integrate the result of monolingual machine translation system with the other available computational linguistic resources of(More)
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(More)
Textual Entailment (TE) is a critical issue in natural language processing (NLP); many NLP applications can be benefited from the recognition of textual entailment (RTE). In this paper we report our observation on how to improve the Chinese textual entailment system and the experiment results on the NTCIR-10 RITE-2 dataset. To complement the traditional(More)
我們所參與公開評測 NTCIR10 RITE-2[5]將文字蘊涵的研究分成兩種層面,首先是分兩 類(Binary Class, BC) ,任務的目標是單純判別 T1 與 T2 之間是否具有蘊涵關係。但句 子之間蘊涵關係並不能單純以有或沒有這麼簡單就區分開,NTCIR RITE 另外定義多類 (Multi Class, MC)這項任務,將句子之間的蘊涵分類為正向、雙向、矛盾、與獨立四種 關係。假設這個句子對具有蘊涵關係,但有可能兩個句子所包涵的資訊數量不同,造成 我們只能從其中一個句子推論出另一個句子的完整的意思,這樣的情況我們稱為兩個句 子間的蘊涵關係為正向蘊涵。反之兩個句子可以互相推論出另一個句子的含意,這樣的 情況我們就稱為雙向蘊涵關係。假設句子對之間沒有蘊涵關係,我們可以很合理認為兩(More)
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