Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Natural Language Processing with Python
Natural Language Processing with Python
Joint inference for cross-document information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Multimodal Speaker Diarization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speaker Diarization: A Review of Recent Research
IEEE Transactions on Audio, Speech, and Language Processing
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We present a fully automatic system from raw data gathering to navigation over heterogeneous news sources, including over 18k hours of broadcast video news, 3.58M online articles, and 430M public Twitter messages. Our system addresses the challenge of extracting "who," "what," "when," and "where" from a truly multimodal perspective, leveraging audiovisual information in broadcast news and those embedded in articles, as well as textual cues in both closed captions and raw document content in articles and social media. Performed over time, we are able to extract and study the trend of topics in the news and detect interesting peaks in news coverage over the life of the topic. We visualize these peaks in trending news topics using automatically extracted keywords and iconic images, and introduce a novel multimodal algorithm for naming speakers in the news. We also present several intuitive navigation interfaces for interacting with these complex topic structures over different news sources.