|19th European Summer School in Logic, Language and Information|
Week 1: Tuesday, 7 August
Speaker: Alexander Koller
Title: Some thoughts about a computational semantics for the 21st century
Abstract: The history of computational natural-language semantics over the past thirty years can be seen as a success story in implementing formal semantics on
the computer. At the beginning of the 21st century, we have access to well-understood representation formalisms, semantics construction algorithms based on large-scale
grammars, we have methods of dealing very efficiently with certain types of ambiguity, and our Logic & Computation colleagues are continuously refining the
efficiency of theorem provers that computational semanticists can use to compute inferences.
Week 1: Thursday, 9 August
Speaker: Michael Witbrock
Title: Logic, Knowledge and Intelligence
Abstract: One aspect of Cyc is a very large, logic-based knowledge base, but it is more than that; the Cyc project is an attempt to move us towards general Artificial Intelligence by supporting automated reasoning about a very wide variety of real-world concerns. To support that goal, Cyc also encompasses, obviously enough, and inference engine able to reason over a large, contextual, knowledge base, but it also includes components for interpreting and producing natural language, acquiring knowledge and responding to user queries, and for interfacing with other software. In this talk, I'll talk about some of what we've done to apply logic to representation of general knowledge, at scale, and to use it in the production of (somewhat) intelligent behaviours, and discuss some ways in which we might move closer to artificial intelligence.
Week 2: Tuesday, 14 August
Speaker: Ede Zimmermann
Title: Painting and Opacity
Abstract: Referentially opaque verbs like "seek", "owe", and "resemble" are known to (a) allow for an ambiguity between an ordinary (specific) and a peculiar unspecific reading of one of their nominal arguments; (b) defy truth-preserving substitution of co-extensional terms; and (c) block existential inferences: (a) one may seek/owe someone/resemble a horse without necessarily seeking/owing/resembling a particular animal; (b) even if all and only arctic horses are striped, one may seek/owe someone/resemble a striped horse without seeking/owing/resembling an arctic horse and (c) without there necessarily even being any striped (i.e. arctic) horses. Similarly, it would seem, one can paint, draw or imagine a striped horse without (a) portraying a particular animal, or (b) painting an arctic horse, or (c) there being any striped, or arctic, horses. As consequence of this analogy, verbs of depiction (like "paint", "draw", and "imagine") have often been taken to be opaque. In particular it has been suggested that the same mechanism that explain the anomalies (a)-(c) of opaque verbs, carry over to them. In this talk I will show that none of the known semantic analyses of opacity extends to verbs of depiction and present evidence that they are simply verbs of creation (as in "paint a picture") that invite a meaning shift of the nominal object as denoting representations of its ordinary denotations (e.g. horse pictures instead of horses).
Week 2: Thursday, 16 August
Speaker: Ronan Reilly
Title: A model of grammar acquisition: the importance of complexity
Abstract: This talk will discuss some recent finding in the computer modelling of language acquisition. Contrary to the view enshrined in the poverty of the stimulus argument, I will demonstrate that a grammar as complex as that found in child-directed speech can be learnt by tuning into its statistical properties using a simple recurrent network (SRN). The SRN succeeds in learning a complex grammar and exhibits behaviours comparable to those found in child language development. This demonstrates that statistical information is sufficient to learn the syntactic structures and categories underlying language and that statistical learning is a feasible mechanism for children to employ. Surprisingly, the complexity of the grammar does not hinder performance but rather enables the acquisition of abstract grammatical structures that enhance the network's generalisation abilities.