Recurrent neural network classiﬁer for three layer conceptual network and performance evaluation
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CitationRhaman, M. K. (2010). Recurrent neural network classiﬁer for three layer conceptual network and performance evaluation . JOURNAL OF COMPUTERS, 5(1), 40–48. doi:10.4304/jcp.5.1.40-48
Natural language has traditionally been handled using symbolic computation and recursive processes. Classiﬁcation of natural language by using neural network is a hard problem. Past few years several recurrent neural network (RNN) architectures have emerged which have been used for several smaller natural language problems. In this paper, we adopt Elman RNN classiﬁer for disease classiﬁcation for a doctor patient-dialog system. We ﬁnd that the Elman RNN is able to ﬁnd a representation for natural language. Contextual analysis in dialog is also a major problem. A three layers memory structure was adopted to address the challenge which we referred to as ”Three Layer Conceptual Network” (TLCN). This highly efﬁcient network simulates the human brain by discourse information. An extended case structure framework is used to represent the knowledge. We used the same case frame structure to train and examine the RNN classiﬁer. This system prototype is based on doctor-patients dialogs. The over all system performance achieved 84% accuracy. Disease identiﬁcation accuracy depends on number of disease and number of utterances. The performance evaluation is also discussed in this paper.