Lexical entailment is a requirement for success in the domains of Recognizing Textual Entailment (RTE) as well as related tasks like Question-Answering and Information Extraction. Previous approaches tend to fall into two camps - those that make use of distributional models and those that make use of knowledge bases such as WordNet. Interestingly, these methods make very different kinds of mistakes and so in this thesis, we construct a new entailment measure by combining these two paradigms in such a way that exploits their differences. We also experiment with including local context and modify an existing approach to achieve the best unsupervised performance so far on the Lexical Substitution task. Overall, we achieve a significant gain in performance on three different evaluations and our approach is also faster than the other distributional approaches we compare to as we are able to avoid fruitless comparisons. Furthermore, we introduce a new approach to evaluate lexical entailment that avoids some of the issues of wordlists - the current conventional way of evaluating lexical entailment, and that can be additionally used to evaluate lexical entailment in context - a novel task introduced in this paper. We also include experiments that show our new lexical entailment model improves performance on the RTE task, the main goal of this work.
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