ESSLLI 2005 - Evening lectures

The EVENING LECTURES given by renowned figures in the field: Mark Johnson, Mark Steedman, Jane Hillston and Peter Gardenfoers.
A student posters session.
The FOLLI general meeting.

Details and dates of these events can be found below.

First Week: 8 - 12th of August, 2005
Date: Tuesday, August 9th
	Time: 21.00 Room: Lecture Theatre 4 Speaker: Mark Johnson Title: Statistics and the scientific study of language Abstract: Since the "statistical revolution" in the mid-1990s, statistical methods have dominated computational linguistics. But what, if anything, do they have to do with the scientific study of language? In this talk I'll discuss the relationship between statistics, logic and language, and what all this has to do with the scientific study of language. Along the way I'll introduce examples from parsing and learning.
Date: Thursday, August 11th
	Time: 20.30 Title: FoLLI General Meeting. Agenda: can be found here.
	Time: 21.00 Room: Lecture Theatre 4 Speaker: Mark Steedman Title: Grammar Acquisition by Child and Machine Abstract: The talk draws attention to some similarities between the problems of inducing wide coverage grammars and statistical models from treebanks on the one hand, and child language learning on the other. Thus: The only way that anyone has so far been able to induce reasonably sound, wide coverage, adult-sized grammars for realistic corpora, by machine, is via Supervised Learning, based on Human-annotated data, such as that in the Penn Wall Street Journal corpus. The only way that anyone has been able to write programs that parse accurately using grammars of that size and ambiguity is by using statistical models based on the same labeled data, such as Head-Dependency models. Such models work because they reflect a mixture of semantic and knowledge-based information. Likewise: The only plausible source for the positive evidence that the child brings to bear on the induction of grammar from strings is access to meaning representations. The only plausible source for negative evidence is statistical properties of the corpus the child is exposed to There is clear evidence that human sentence processing relies on a model of semantic, inferential, and pragmatic coherence for ambiguity resolution. The talk argues that existing computational models of the probabilistic acquisition of grammars and models from meaning representations offer a simpler and less stipulative account of child language acquisition than standard psycholinguistic accounts. In particular, they suggest that standard notions of "trigger" and "parameter setting" (and their attendant homunculi) are redundant. It also argues that models of child language acquisition can inform the much harder task of semi-supervised induction of wide-coverage parsers from large volume unlabeled text.
Second Week: 15 - 19th of August, 2005
Date: Monday, August 15th
	Time: 21.00-22.00 Title: Poster sessions
Date: Tuesday, August 16th
	Time: 21.00 Room: Lecture Theatre 4 Speaker: Jane Hillston Title: Getting performance out of process algebra Abstract: Process algebras are system description techniques supported by apparatus for formal reasoning. When extended with data about durations and probabilities they can be used to derive quantitative as well as qualitative properties of systems. PEPA is a stochastic process algebra in which quantification has been added in such a way as to allow quantitative reasoning to be carried out in terms of an underlying Markov process. In this talk I will discuss the design of the PEPA language, the interplay between the process algebra and the Markov process, and how properties of both can be exploited when carrying out quantitative analysis.
Date: Thursday, August 18th
	Time: 21.00 Room: Lecture Theatre 4 Speaker: Peter Gaerdenfors Title: How to make the Semantic Web more Semantic Abstract: The Semantic Web is not semantic. It is good for syllogistic reasoning, but there is much more to semantics than syllogisms. I argue that the current Semantic Web is too dependent on symbolic representations of information structures, which limits its representational capacity. As a remedy, I propose conceptual spaces as a tool for expressing more of the semantics. Conceptual spaces are built up from quality dimensions that have geometric or topological structures. With the aid of the dimensions, similarities between objects can easily be represented and it is argued that similarity is a central aspect of semantic content. By sorting the dimensions into domains, I define properties and concepts and show how prototype effects of concepts can be treated with the aid of conceptual spaces. I present an outline of how one can reconstruct most of the taxonomies and other meta-data that are explicitly coded in the current Semantic Web and argue that inference engines on the symbolic level will become largely superfluous. As an example of the semantic power of conceptual spaces, I show how concept combinations can be analysed in a much richer and more accurate way than in the classical logical approach.

2005-08-10