We have recently proposed Recognizing Textual Entailment (RTE) as a generic task that captures major semantic inferences across different natural language processing applications. The talk will first review the motivation and definition of the textual entailment task and the PASCAL RTE-1,2&3 Challenges benchmarks. Then we will demonstrate directions for building textual entailment systems, based on knowledge acquisition and inference, and for utilizing them within concrete applications. Furthermore, we suggest that textual entailment modeling may become a comprehensive framework for applied semantics research. Such framework introduces useful variants of known semantic problems and highlights important tasks which were hardly investigated so far at an applied computational level. The semantic modeling perspective will be illustrated in more detail by a case study for an entailment-based variant of word sense disambiguation.