Abstract
A common task in natural language processing and text mining is the extraction and formatting of information from unstructured text. One can think of the end goal of information extraction in terms of filling templates codifying the extracted information. The templates are then put into a knowledge database for future use. This chapter describes several models and learning methods that can be used to solve information extraction. We focused on two major subtasks, one is to extract entities, such as person name, organization, etc. from sentences, and the other is to determine the relationship among extracted entities.
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