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book chapter · Springer Actuarial · 2022

Natural Language Processing

Mario V. Wüthrich, Michael Merz

Cited by 4Open access

Abstract

Abstract This chapter discusses natural language processing (NLP) which deals with regression modeling of non-tabular or unstructured text data. We explain how words can be embedded into low-dimension spaces that serve as numerical word encodings. These can then be used for text recognition, either using RN networks or attention layers. We give an example where we aim at predicting claim perils from claim descriptions.

Cite this paper

APA 7

Wüthrich, M. V., & Merz, M. (2022). Natural Language Processing. Springer Actuarial, 425–452. https://doi.org/10.1007/978-3-031-12409-9_10

MLA 9

Wüthrich, Mario V., and Michael Merz. “Natural Language Processing.” Springer Actuarial, 2022, pp. 425–52, https://doi.org/10.1007/978-3-031-12409-9_10.

Chicago

Wüthrich, Mario V., and Michael Merz. 2022. “Natural Language Processing.” Springer Actuarial: 425–52. https://doi.org/10.1007/978-3-031-12409-9_10.

Harvard

Wüthrich, M.V. and Merz, M. (2022) ‘Natural Language Processing’, Springer Actuarial, pp. 425–452. Available at: https://doi.org/10.1007/978-3-031-12409-9_10.

BibTeX
@incollection{wuthrich2022natural,
  title = {{Natural Language Processing}},
  author = {Wüthrich, Mario V. and Merz, Michael},
  booktitle = {Springer Actuarial},
  pages = {425--452},
  year = {2022},
  doi = {10.1007/978-3-031-12409-9_10}
}

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