Staff

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.

Speaker: Nasser Mooman

With the increasing amount of available information sources for learners, the need for systems that can effectively and efficiently mine, retrieve, and process such information has become critical.

Speaker: Linli Xu

In this talk, I will discuss a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM.

Speaker: Mu Zhu (Statistics and Actuarial Sciences, University of Waterloo)

I shall describe a few projects in the area of rare target detection.

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.

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.

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.

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.