A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order 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, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.

Effectiveness of pre- and inprocessing for CDCL-based SAT solving

Applying pre- and inprocessing techniques to simplify CNF formulas both before and during search can considerably improve the performance of modern SAT solvers. These algorithms mostly aim at reducing the number of clauses, literals, and variables in the formula. However, to be worthwhile, it is necessary that their additional runtime does not exceed the runtime saved during the subsequent SAT solver execution. In this paper we investigate the efficiency and the practicability of selected simplification algorithms for CDCL-based SAT solving. We first analyze them by means of their expected impact on the CNF formula and SAT solving at all. While testing them on real-world and combinatorial SAT instances, we show which techniques and combinations of them yield a desirable speedup and which ones should be avoided.