CYC, WordNet, and EDR: critiques and responses
Communications of the ACM
Why machines should analyse intention in natural language dialogue
International Journal of Human-Computer Studies
Performance issues and error analysis in an open-domain question answering system
ACM Transactions on Information Systems (TOIS)
Deploying an agent-based architecture for the management of community care
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Introduction to special issue on machine learning approaches to shallow parsing
The Journal of Machine Learning Research
CAMAS: a citizen awareness system for crisis mitigation
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
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This paper investigates the potential use of dialog-based ALICEbots in disseminating terrorism information to the general public. In particular, we study the acceptance and response satisfaction of ALICEbot responses in both the general conversation and terrorism domains. From our analysis of three different knowledge sets: general conversation, terrorism, and combined, we found that users were more favorable to the systems that exhibited conversational flow. We also found that the system that incorporated both conversation and terrorism knowledge performed better than systems with only conversation or terrorism knowledge alone. Lastly, we were interested in what types of questions were the most prevalently used and discovered that questions beginning with 'wh*' words were the most popular method to start an interrogative sentence. However, 'wh* sentence starters surprisingly proved to be in a very narrow majority.