Recognizing Textual Entailment with Deep-Shallow Semantic Analysis and Logical Inference

In this paper, the architecture and evaluation of a new system for recognizing textual entailment (RTE) is presented. It is conceived as an adaptable and modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis it combines an HPSG-based deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and accuracy of results. For recognizing textual entailment we use logical inference of firstorder employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, experimental sources with, e.g., manually defined presupposition resolutions, or with general and common sense knowledge. The system comes with a graphical user interface for control and presentation purposes. The evaluation shows that the success rate of the presented RTE system is comparable with that of the best logic-based approaches.