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Speaker: Rejean Lau (University of Alberta)

Sentiment classification is a form of the text categorization problem where user sentiment is categorized as either positive or negative sentiment. It is generally accepted that sentiment analysis is a more challenging classification problem then topic categorization and here the bag of words approach does not perform as well. Using the IMDB sentiment dataset from Cornell University, we improve on their results by using a stacked classifier and web-mined features.

Friday, September 23, 2005 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Learning basic block scheduling heuristics from optimal data

Speaker: Tyrel Russell

Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem in instruction scheduling is to find a minimum length schedule for a basic block---a straight-line sequence of code with a single entry point and a single exit point---subject to precedence, latency, and resource constraints. Solving the problem exactly is known to be difficult, and most compilers use a greedy list scheduling algorithm coupled with a heuristic.

Friday, September 30, 2005 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: A notion of stability for sample-based clustering

Speaker: Shai Ben-David

Clustering is one of the most widely used techniques for exploratory data analysis. Across all disciplines, from social sciences over biology to computer science, people try to get a first understanding of their data by identifying meaningful groups among the data points.

Speaker: Kevin Regan

This paper examines approaches to representing uncertainty in reputation systems for electronic markets with the aim of constructing a decision theoretic framework for collecting information about selling agents and making purchase decisions in the context of a social reputation system.

Friday, October 14, 2005 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Ontologies: Mature research tools?

Speaker: Rob Warren

Ontology research has had a renewed interest with the release of two ontological markup standards, DAML+OIL and OWL, in December of 2001 and March of 2002.

In this talk I'll review some of the work I did while at the Fungal Web Project in Montreal, where we faced several questions about the use and adoption of ontologies and their markup languages:

Friday, October 21, 2005 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Why bother about bother: Is it worth it to ask the user?

Speaker: Robin Cohen

In this paper, we discuss the importance of modeling the potential bother to the user, when reasoning about interaction in a mixed-initiative setting. We summarize our previous work on modeling bother as one of the costs of interaction, clarifying how to incorporate this estimated cost when reasoning about whether to initiate interaction.

Friday, October 28, 2005 11:30 am - 11:30 am EDT (GMT -04:00)

AI seminar: Who's asking for help? A Bayesian approach to intelligent assistance

Speaker: Bowen Hui (University of Toronto)

Automated software customization is drawing increasing attention as a means to help users deal with the scope, complexity, potential intrusiveness, and ever-changing nature of modern software. The ability to automatically customize functionality, interfaces, and advice to specific users is made more difficult by the uncertainty about the needs of specific individuals and their preferences for interaction.

Friday, November 4, 2005 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Structuring interactive cluster analysis

Speaker: Wayne Oldford (Dir. of Computational Math, UW)

The problem of cluster analysis, or finding groups in data, is inherently ill-posed; hence the multitude of different methods which purport to solve "the'' problem. In this talk, a variety of examples illustrate this point and cast doubt on whether a single universally useful clustering method exists.

Friday, November 11, 2005 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Beyond integer domains: The all different and global cardinality constraints

Speaker: Claude-Guy Quimper

After giving a brief summary of general principles in constraint programming, we will present two constraints: the all different constraint and the global cardinality constraint.

Friday, November 18, 2005 11:30 am - 11:30 am EST (GMT -05:00)

AI seminar: Dealing with word sense disambiguation in lexical chaining

Speaker: Mattt Enss

A lexical chain is a sequence of words in a document that are semantically related (i.e., related in meaning). Lexical chains indicate where certain topics or subjects are being discussed in a document. The chains therefore can provide context and be used to determine where topic changes occur.