We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the most relevant phrases, and labels the type of information they contain. The approach and preliminary results are demonstrated on a subset of the MUC-6 Scenario Templates task.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"H. Zaragoza"},{"type":"text","value":"P. 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