Online Semantic Extraction by Backpropagation Neural Network with Various Syntactic Structure Representations

Heidi H. T. Yeung

The sub-symbolic approach on Natural Language Processing (NLP) is one of the mainstreams in Artificial Intelligence. Indeed, we have plenty of algorithms for variations of NLP such as syntactic structure representation or lexicon classification theoretically. The goal of these researches is obviously for developing a hybrid architecture which can process natural language as what human does. Thus, we propose an online intelligent system to extract the semantics (utterance interpretation) by applying a 3-layer back propagation neural network to classify the encoded syntactic structures into corresponding semantic frame types (e.g. AGENT_ACTION_PATIENT). The results are generated dynamically according to training sets and user inputs in webpage-form. It can diminish the manipulating time while using extra tools and share the statistical results with colleagues in clear and standard forms.

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