Named Entity Recognition by Neural Sliding Window

  • Gallo I
  • Binaghi E
  • Carullo M
 et al. 
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Abstract

Named Entity Recognition (NER) is an important subtask of document processing such as Information Extraction. This paper describes a NER algorithm which uses a Multi-Layer Perceptron (MLP) to find and classify entities in natural language text. In particular we use the MLP to implement a new supervised context-based NER approach called Sliding Window Neural (SWiN). The SWiN method is a good solution for domains where the documents are grammatically ill-formed and it is difficult to exploit the features derived from linguistic analysis. Experiments indicate good accuracy compared with traditional approaches and demonstrate the system's portability.

Author-supplied keywords

  • Named Entity Recognition
  • neural network

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Authors

  • I. Gallo

  • E. Binaghi

  • M. Carullo

  • N. Lamberti

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