Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of an extraction-based approach to answering definitional questions
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Extracting important sentences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The world of mushrooms: human-computer interaction prototype systems for ambient intelligence
Proceedings of the 9th international conference on Multimodal interfaces
International Journal of Human-Computer Studies
Effects of conversational agents on human communication in thought-evoking multi-party dialogues
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A self-supervised approach for extraction of attribute-value pairs from wikipedia articles
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
User-adaptive coordination of agent communicative behavior in spoken dialogue
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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This paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate "Who is this?" quizzes. The definitions are ordered according to how difficult they make it to name the person. Such ranking would enable users to interactively learn about a person through dialogue with a system with improved understanding and lasting motivation, which is useful for educational systems. In our approach, we train a ranker that learns from data the appropriate ranking of definitions based on features that encode the importance of keywords in a definition as well as its content. Experimental results show that our approach is significantly better in ranking definitions than baselines that use conventional information retrieval measures such as tf*idf and pointwise mutual information (PMI).