Making Sense Out of the Web

In the past few years, we have witnessed a tremendous growth of the World Wide Web, both in terms of number of Web pages accessible online – resulting in what represents today the largest publicly available corpus, and in terms of number of Web users – who now form the world’s largest pool of knowledge. The growth of the Web on these two main dimensions – pages and users – has opened the doors to a realm of new approaches to data-hungry and knowledge-hungry language processing applications. Among these, Word Sense Disambiguation is one of the applications that has the potential of benefiting the most from the large amounts of Web-based data and from the availability of inexpensive Web user supervision. In this paper, I overview the main lines of research in deriving efficient Word Sense Disambiguation methods that exploit: (1) the Web as a corpus – which represents a view of the Web seen as an enormous collection of Web pages; and (2) the Web as collective mind – where the Web is regarded as a large group of Web users who can contribute their knowledge to the process of identifying word meanings.