Textual Entailment has been proposed recently as a generic framework for modeling semantic variability in many Natural Language Processing applications, such as Question Answering, Information Extraction, Information Retrieval and Document Summarization. The Textual Entailment relationship holds between two text fragments, termed text and hypothesis, if the truth of the hypothesis can be inferred from the text.
In this talk, the Textual Entailment framework will be introduced. I’ll then present an algorithm for large-scale Web-based acquisition of entailment rules, a type of knowledge needed for robust inference. Finally, I will present an unsupervised Relation Extraction approach based on the Textual Entailment framework.