This thesis introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. We define the task and describe the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. This dataset was the basis for the PASCAL Recognising Textual Entailment (RTE) Challenge. We further describe how textual entailment can be approximated and modeled at the lexical level and propose a lexical reference subtask and a correspondingly derived dataset.

The thesis further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. We suggest that the proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. In addition, we describe two lexical models demonstrating the applicability of the probabilistic setting. Although our proposed models are relatively simple, as they do not rely on syntactic or other deeper analysis, they nevertheless achieved competitive results on the pascal rte challenge. Finally, the thesis presents a novel acquisition algorithm to identify lexical entailment relations from a single corpus focusing on the extraction of verb paraphrases. Most previous approaches detect individual paraphrase instances within a pair (or