By Sbihi A.

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6. The aim is to learn a pattern recognizer from a small number of labeled examples and to improve the classifier by using the unlabeled examples, or at least learn a classifier whose performance is equal to one trained on the full labeled set. In addition, there are approaches that aim at eliminating the need for manual annotation entirely. Most of the work in machine learning regards the acquisition of patterns for entity classification, entity relation recognition, semantic role classification, and recognition and resolution of temporal expressions.

The different models that are discussed are the classical vector space model, the language model, the inference network model and the logic based model. Because information extraction leaves behind a bag-of-words representation of query and documents, we need adapted indexing structures that can be efficiently searched at the time of querying. Chapter 8 discusses the evaluation metrics currently in use in information extraction. Evaluation metrics allow a comparison of technologies and systems.

The analysis of the story in terms of plot units results in a complex network where some units are subordinated to others. These graph type representations of a story structure still have their influence today (Mani, 2003). Interesting also to mention is The Linguistic String Project (LSP) at New York University, which began in 1965 with funding from the National Science Foundation to develop computer methods for structuring and accessing information in the scientific and technical literature. Document processing was to be based on linguistic principles, first to demonstrate the possibility of computerized grammatical analysis (parsing), then to extend to specialized vocabulary and rules for particular scientific domains.

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