It is a truism that as the Web grows in size and scope, it becomes harder to find what we want, to identify like-minded people and communities, to find the best ads to offer, and to have applications work together smoothly. Services don't interoperate; queries yield long lists of results, most of which seem to miss the point. If the Web were a person, we would expect richer and more successful interactions with it - interactions that were, quite literally, more meaningful. That's because in human discourse, it is shared meaning that gives us real communication. Yet with the current Web, meaning cannot be found.Much recent work has aspired to change this, both for human-machine interchange and machine-machine synchronization. Certainly the "semantic web" looks to add meaning to our current simplistic matching of mere strings of characters against mere "bags" of words. But can we legislate meaning from on high? Isn't meaning organic and determined by use, a moving and context-dependent target? But if meaning is an evolving organic soup, how are humans able to get anything done with one another? Don't we love to "define our terms"? But then again, is real definition even possible?These questions have daunted philosophers for years, and we probably won't solve them here. But we'll try to understand what's at the root of our own current religious debate: should meaning on the Web be evolutionary, driven organically through the bottom-up human assignment of tags? Or does it need to be carefully crafted and managed by a higher authority, using structured representations with defined semantics? Without picket signs or violence (we hope), our panelists will explore the two extreme ends of the spectrum - and several points in between.

In previous papers we presented methods for retrieving collocations from large samples of texts.We described a tool, Xtract, that implements these methods and able to retrieve a wide range of collocations in a two stage process.These methods a.s well as other related methods however have some limitations.Mainly, the produced collocations do not include any kind of functional information and many of them are invalid.In this paper we introduce methods that address these issues.These methods are implemented in an added third stage to Xtract that examines the set of collocations retrieved during the previous two stages to both filter out a number of invalid collocations and add useful syntactic information to the retained ones.By combining parsing and statistical techniques the addition of this third stage has raised the overall precision level of Xtract from 40% to 80% With a precision of 94%.In the paper we describe the methods and the evaluation experiments.

Collocational knowledge is necessary for language generation. The problem is that collocations come in a large variety of forms. They can involve two, three or more words, these words can be of different syntactic categories and they can be involved in more or less rigid ways. This leads to two main difficulties: collocational knowledge has to be acquired and it must be represented flexibly so that it can be used for language generation. We address both problems in this paper, focusing on the acquisition problem. We describe a program, Xtract, that automatically acquires a range of collocations from large textual corpora and we describe how they can be represented in a flexible lexicon using a unification based formalism.

In contrast to other kinds of libraries, software libraries need to be conceptually organized. When looking for a component, the main concern of users is the functionality of the desired component; implementation details are secondary. Software reuse would be enhanced with conceptually organized large libraries of software components. In this paper, we present GURU, a tool that allows automatical building of such large software libraries from documented software components. We focus here on GURU's indexing component which extracts conceptual attributes from natural language documentation. This indexing method is based on words' co-occurrences. It first uses EXTRACT, a co-occurrence knowledge compiler for extracting potential attributes from textual documents. Conceptually relevant collocations are then selected according to their resolving power, which scales down the noise due to context words. This fully automated indexing tool thus goes further than keyword-based tools in the understanding of a document without the brittleness of knowledge based tools. The indexing component of GURU is fully implemented, and some results are given in the paper.