Many ways of dealing with large collections of linguistic information
involve the general principle of mapping words, larger terms and
documents into some sort of abstract space. Considerable effort has been
devoted to applying such techniques for practical tasks such as
information retrieval and word-sense disambiguation. However, the
inherent structure of these spaces is often less well-understood. 

Visualisation tools can help to uncover the relationships between
meanings in this space, giving a clearer picture of the natural
structure of linguistic information. We present a variety of tools for
visualising word-meanings in vector spaces and graph models, derived
from co-occurrence information and local syntactic analysis. Our
techniques suggest new solutions to standard problems such as
automatic management of lexical resources, which perform well under
evaluation.

The tools presented in this paper are all available for public
use on our website.