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» A Hidden Markov Model Information Retrieval System
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ICML
2000
IEEE
16 years 5 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
EMNLP
2004
15 years 5 months ago
Comparing and Combining Generative and Posterior Probability Models: Some Advances in Sentence Boundary Detection in Speech
We compare and contrast two different models for detecting sentence-like units in continuous speech. The first approach uses hidden Markov sequence models based on N-grams and max...
Yang Liu, Andreas Stolcke, Elizabeth Shriberg, Mar...
AAAI
2000
15 years 5 months ago
Information Extraction with HMM Structures Learned by Stochastic Optimization
Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction--the task of populating database slots with corresponding phrases...
Dayne Freitag, Andrew McCallum
ICIP
2003
IEEE
16 years 5 months ago
Stochastic attributed K-d tree modeling of technical paper title pages
Structural information about a document is essential for structured query processing, indexing, and retrieval. A document page can be partitioned into a hierarchy of homogeneous r...
Song Mao, Azriel Rosenfeld, Tapas Kanungo
ECCV
2000
Springer
16 years 6 months ago
A Probabilistic Background Model for Tracking
A new probabilistic background model based on a Hidden Markov Model is presented. The hidden states of the model enable discrimination between foreground, background and shadow. Th...
Jens Rittscher, Jien Kato, Sébastien Joga, ...