Named Entity Recognition by Neural Sliding Window

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.

• Type: Conference
• Year: 2008
• Where: DAS
• Authors: Ignazio Gallo, Elisabetta Binaghi, Moreno Carullo, Nicola Lamberti
• Keywords: DAS 2008, Document Analysis, Named Entity Recognition, NER Algorithm, Supervised Context-based Ner