Analyses of Negative Entailment Phenomena for Textual Entailment Recognition

• Main Authors: Kai-Chun Chang, 張凱淳
• Other Authors: Hsin-Hsi Chen
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/27038742486471704885

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === The researches on Textual Entailment (TE) have attracted much attention in recent years. RTE (Recognising Textual Entailment in short), a series of evaluations which focus on the developments of English textual entailment recognition technologies, has been held 7 times up to 2011. In 2011, the 9th NTCIR Workshop Meeting first introduced a Textual Entailment task called RITE (Recognizing Inference in TExt in short) into the IR series evaluation. RITE focuses on the Textual Entailment researches in Traditional Chinese, Simplified Chinese, and Japanese. The first ground truth and text-hypothesis pair data set in both Traditional and Simplified Chinese have been distributed. In this thesis, we concentrate on what kind of phenomena in text-hypothesis pairs would be powerful features to deal with the textual entailment problem. After analyzing and experiments on the dataset distributed by Mark Sammons et al., which is annotated with linguistic phenomena defined by Mark’s research group, we found that the Negative Entailment Phenomena is the most powerful aspect in textual entailment. Accuracy more than 90% was achieved. In this aspect, there are five outstanding phenomena including Disconnected Relation, Exclusive Argument, Exclusive Relation, Missing Argument, and Missing Relation. Then, we tried to extract the linguistic phenomena from text-hypothesis pairs automatically. Two automatic methods, i.e., rule-based method and machine-learning method, were employed. After applying the phenomena extracted by these automatic methods as features in the TE experiments, the results show that there is a large gap between the human-annotated phenomena and machine-extracted phenomena. There is still room for improvement with this kind of features. All the above analyses and experiments were made on English data. We aim at knowing whether these important phenomena are also effective in dealing the TE problems on Chinese data or not. Following the similar scheme in English, we annotate the BC-CT text-hypothesis pairs distributed by NTCIR-9 RITE task with the five phenomena. The experiments on both human-annotated and machine-extracted features show these negative entailment phenomena still do well in Chinese.