An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Multimodal Speaker Diarization
IEEE Transactions on Pattern Analysis and Machine Intelligence
An overview of automatic speaker diarization systems
IEEE Transactions on Audio, Speech, and Language Processing
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With the evolution of online communication methods, conversations are increasingly handled via email, internet forums and other such methods. In this paper, we attempt to model lexical information in a context sensitive manner, encoding our belief that the use of language depends on the participants in the conversation. We model the discourse as a combination of the speaker, the addressee and other participants in the conversation as well as a context specific language model. In order to do this, we introduce a novel method based on an HMM with an exponential state space to capture speaker-addressee context. We also study the performance of topic modeling frameworks in conversational settings. We evaluate the models on the tasks of identifying the set of people present in any conversation, as well as identifying the speaker for every utterance in the conversation, and they show significant improvement over the baseline models.