On-Line Error Detection of Annotated Corpus Using Modular Neural Networks

This paper proposes an on-line error detecting method for a manually annotated corpus using min-max modular (M3 ) neural networks. The basic idea of the method is to use guaranteed convergence of the M3 network to detect errors in learning data. To confirm the effectiveness of the method, a preliminary computer experiment was performed on a small Japanese corpus containing 217 sentences. The results show that the method can not only detect errors within a corpus, but may also discover some kinds of knowledge or rules useful for natural language processing.

• Type: Conference
• Year: 2001
• Where: ICANN
• Authors: Qing Ma, Bao-Liang Lu, Masaki Murata, Michinori Ichikawa, Hitoshi Isahara
• Keywords: Error Detecting Method, ICANN 2001, M3 Network, Neural Networks, Small Japanese Corpus