Five papers on Wordnet

TL;DR: This chapter discusses the infrastructure of the Web, the future of Web mining, and applications of semi-supervised learning for text and similarity and clustering.

TL;DR: This paper describes the approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank and introduces metaframes as a technique for handling similar frames among near− synonymous verbs.

TL;DR: In this paper, a computerized method for retrieving documents from a text corpus in response to a user-supplied natural language input string, e.g., a question, is presented.

TL;DR: This work gives formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities, and investigates practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters.