Named Entity Recognition by Neural Sliding Window
Ignazio Gallo, Elisabetta Binaghi, Moreno Carullo and Nicola Lamberti
In: DAS 2008: The Eighth IAPR Workshop on Document Analysis Systems, September 16-19, 2008, Nara, Japan.
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.