Hierarchical hidden markov models for information extraction

@misc{citeulike:2087320, abstract = {Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learning methods to induce models for extracting relation instances from biomedical articles. We propose and evaluate an approach that is based on using hierarchical hidden Markov models to represent the grammatical structure of the sentences being processed. Our approach first uses a shallow parser to construct a...}, author = {Skounakis, M. and Craven, M. and Ray, S. }, citeulike-article-id = {2087320}, keywords = {hmm, work}, posted-at = {2007-12-11 00:46:49}, priority = {2}, title = {Hierarchical hidden markov models for information extraction}, url = {http://citeseer.ist.psu.edu/605107.html}, year = {2003} }

TY - GEN ID - citeulike:2087320 L3 - citeulike-article-id:2087320 TI - Hierarchical hidden markov models for information extraction N2 - Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learning methods to induce models for extracting relation instances from biomedical articles. We propose and evaluate an approach that is based on using hierarchical hidden Markov models to represent the grammatical structure of the sentences being processed. Our approach first uses a shallow parser to construct a... KW - hmm KW - work AU - Skounakis, M AU - Craven, M AU - Ray, S PY - 2003/// UR - http://citeseer.ist.psu.edu/605107.html ER -