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ICML
2000
IEEE
16 years 2 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...
OSDI
2008
ACM
16 years 2 months ago
Hunting for Problems with Artemis
Artemis is a modular application designed for analyzing and troubleshooting the performance of large clusters running datacenter services. Artemis is composed of four modules: (1)...
Gabriela F. Cretu-Ciocarlie, Mihai Budiu, Mois&eac...
ICAIL
2005
ACM
15 years 7 months ago
Automatic Legal Text Summarisation: Experiments with Summary Structuring
We describe a set of experiments using machine learning techniques for the task of extractive summarisation. The research is part of a summarisation project for which we use a cor...
Ben Hachey, Claire Grover
SIGIR
2003
ACM
15 years 7 months ago
Text categorization by boosting automatically extracted concepts
Term-based representations of documents have found widespread use in information retrieval. However, one of the main shortcomings of such methods is that they largely disregard le...
Lijuan Cai, Thomas Hofmann
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
15 years 7 months ago
Extraction of informative genes from microarray data
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples...
Topon Kumar Paul, Hitoshi Iba