Year: 2005
Type: conference-paper
Abstract: We describe our efforts to generate a large (100,000 instance) corpus of textual entailment pairs from the lead paragraph and headline of news articles. We manually inspected a small set of news stories in order to locate the most productive source of entailments, then built an annotation interface for rapid manual evaluation of further exemplars. With this training data we built an SVM-based document classifier, which we used for corpus refinement purposes---we believe that roughly three-quarters of the resulting corpus are genuine entailment pairs. We also discuss the difficulties inherent in manual entailment judgment, and suggest ways to ameliorate some of these.
Authors: John Burger, Lisa Ferro
Institution: Mitre (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2006647942