His research interests for over a decade have been within empirical and learning methods for language processing, often operating over rich linguistic representations, with particular emphasis on unsupervised semantic learning. In the last few years he has introduced textual entailment as a generic framework for applied semantic inference over texts. With our colleagues, we organized the three rounds of the PASCAL Recognizing Textual Entailment (RTE) Challenges (2004-2007), which attracted dozens of research groups and became the primary forum for empirical evaluation of semantic inference systems. At the Bar Ilan NLP group he develops computational models of textual entailment, including automatic knowledge acquisition, semantic inference, and information extraction and retrieval applications.