Abstract
This chapter describes in detail a new approach for exploiting preexisting datasets to support Information Extraction by Text Segmentation methods. First, it presents a brief overview of the approach and introduces the concept of knowledge base. Next, it discusses all the steps involved in the unsupervised approach, including how to learn content-based features from knowledge bases, how to automatically induce structure-based features with no previous human-driven training, a feature that is unique to this approach, and how to effectively combine these features to label segments of a text input.
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Notes
- 1.
The maximum probability density of \(v_A\) is \(1/\sqrt{2\pi \sigma ^{2}}\).
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Cortez, E., da Silva, A.S. (2013). Exploiting Pre-Existing Datasets to Support IETS. In: Unsupervised Information Extraction by Text Segmentation. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02597-1_3
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