Abstract

Textual Entailment (TE) aims at capturing major semantic inference needs across applications in Natural Language Processing. Since 2005, in the TE recognition (RTE) task, systems are asked to automatically judge whether the meaning of a portion of text, the Text, entails the meaning of another text, the Hypothesis. Although several approaches have been experimented, and improvements in TE technologies have been shown in RTE evaluation campaigns, a renewed interest is rising in the research community towards a deeper and better understanding of the core phenomena involved in textual inference. In line with this direction, we are convinced that crucial progress may derive from a focus on decomposing the complexity of the TE task into basic phenomena and on their combination. Analysing TE in the light of the notions provided in logic to define an argument, and to evaluate its validity, the aim of our work is to understand how the common intuition of decomposing TE would allow a better comprehension of the problem from both a linguistic and a computational viewpoint. We propose a framework for component-based TE, where each component is in itself a complete TE system, able to address a TE task on a specific phenomenon in isolation. Five dimensions of the problem are investigated: i) the definition of a component-based TE architecture; ii) the implementation of TE-components able to address specific inference types; iii) the linguistic analysis of the phenomena relevant to component-based TE; iv) the automatic acquisition of knowledge to support component-based entailment judgements; v) the development of evaluation methodologies to assess component-based TE systems capabilities to address single phenomena in a pair.