An automatic procedure for topic-focus identification
Computational Linguistics
Map displays for information retrieval
Journal of the American Society for Information Science
A vector space model for automatic indexing
Communications of the ACM
Self-Organizing Maps
ICA and SOM in text document analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
An Efficiently Focusing Large Vocabulary Language Model
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Visualization for the document space
VIS '92 Proceedings of the 3rd conference on Visualization '92
Adaptive dialogue systems - interaction with interact
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Adaptive dialogue systems - interaction with interact
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
Goal detection from natural language queries
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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In human-computer interaction systems using natural language, the recognition of the topic from user's utterances is an important task. We examine two different perspectives to the problem of topic analysis needed for carrying out a successful dialogue. First, we apply self-organized document maps for modeling the broader subject of discourse based on the occurrence of content words in the dialogue context. On a Finnish corpus of 57 dialogues the method is shown to work well for recognizing subjects of longer dialogue segments, whereas for individual utterances the subject recognition history should perhaps be taken into account. Second, we attempt to identify topically relevant words in the utterances and thus locate the old information ('topic words') and new information ('focus words'). For this we define a probabilistic model and compare different methods for model parameter estimation on a corpus of 189 dialogues. Moreover, the utilization of information regarding the position of the word in the utterance is found to improve the results.