Franco Salvetti

Weblog Classification for Fast Splog Filtering: A URL Language Model Segmentation Approach

This paper shows that in the context of statistical weblog classification for splog filtering based on n-grams of tokens in the URL, further segmenting the URLs beyond the standard punctuation is helpful. Many splog URLs contain phrases in which the words are glued together in order to avoid splog filtering techniques based on punctuation segmentation and unigrams. A technique which segments long tokens into the words forming the phrase is proposed and evaluated. The resulting tokens are used as features for a weblog classifier whose accuracy is similar to that of humans (78% vs. 76%) and reaches 93.3% of precision in identifying splogs with recall of 50.9%.

Opinion Polarity Identification of Movie Reviews

One approach to the assessment of overall opinion polarity (OvOP) of reviews, a concept defined in this paper, is the use of supervised machine learning mechanisms. In this paper, the impact of lexical feature selection and feature generalization, applied to reviews, on the precision of two probabilistic classifiers (Naive Bayes and Markov Model) with respect to OvOP identification is observed. Feature generalization based on hypernymy as provided by WordNet, and feature selection based on part-ofspeech (POS) tags are evaluated. A ranking criterion is introduced, based on a function of the probability of having positive or negative polarity, which makes it possible to achieve 100% precision with 10% recall. Movie reviews are used for training and testing the probabilistic classifiers, which achieve 80% precision.

Chaotic time series prediction for the game, Rock-Paper-Scissors

Two players of Rock-Paper-Scissors are modeled as adaptive agents which use a reinforcement learning algorithm and exhibit chaotic behavior in terms of trajectories of probability in mixed strategies space. This paper demonstrates that an external super-agent can exploit the behavior of the other players to predict favorable moments to play against one of the other players the symbol suggested by a sub-optimal strategy. This third agent does not affect the learning process of the other two players, whose only goal is to beat each other. The choice of the best moment to play is based on a threshold associated with the Local Lyapunov Exponent or the Entropy, each computed by using the time series of symbols played by one of the other players. A method for automatically adapting such a threshold is presented and evaluated. The results show that these techniques can be used effectively by a super-agent in a game involving adaptive agents that exhibit collective chaotic behavior.

Local flow betweenness centrality for clustering community graphs

The problem of information flow is studied to identify de facto communities of practice from tacit knowledge sources that reflect the underlying community structure, using a collection of instant message logs. We characterize and model the community detection problem using a combination of graph theory and ideas of centrality from social network analysis. We propose, validate, and develop a novel algorithm to detect communities based on computation of the Local Flow Betweenness Centrality. Using LFBC, we model the weights on the edges in the graph so we can extract communities. We also present how to compute efficiently LFBC on relevant edges without having to recalculate the measure for each edge in the graph during the process. We validate our algorithms on a corpus of instant messages that we call MLog. Our results demonstrate that MLogs are a useful source for community detection that can augment the study of collaborative behavior.

AAAI 2006 Spring Symposium Reports

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford Universitys Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California. The titles of the eight symposia were (1) Argumentation for Consumers of Health Care (chaired by Nancy Green); (2) Between a Rock and a Hard Place: Cognitive Science Principles Meet AI Hard Problems (chaired by Christian Lebiere); (3) Computational Approaches to Analyzing Weblogs (chaired by Nicolas Nicolov); (4) Distributed Plan and Schedule Management (chaired by Ed Durfee); (5) Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (chaired by Chitta Baral); (6) Semantic Web Meets e-Government (chaired by Ljiljana Stojanovic); (7) To Boldly Go Where No Human-Robot Team Has Gone Before (chaired by Terry Fong); and (8) What Went Wrong and Why: Lessons from AI Research and Applications (chaired by Dan Shapiro).

Information flow using edge stress factor

This paper shows how a corpus of instant messages can be employed to detect de facto communities of practice automatically. A novel algorithm based on the concept of Edge Stress Factor is proposed and validated. Results show that this approach is fast and effective in studying collaborative behavior.