PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:5252
Deposited By:Ignazio Gallo
Deposited On:24 March 2009