Summary of Demonstration

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. Implementation This intelligent system is implemented with JAVA which is an object-oriented and multiplatform programming language. During the development stage, the programs can be simply tested in console mode with using IDE provided by SUN. Later, the well-designed Java Service Page (JSP) as the interactive user interface to link those classes as Java Beans with the support of Apache Tomcat server. We use three connectionist-based encoders to represent the syntactic structures: Recursive Auto-Association Memories (RAAM) [Pollack 1990], Sequential RAAM [Kwasny 1993] and Tensor product representation with Non-linear Compression (our proposed representational algorithm). There is a main program “NLP ” which includes all procedures in the semantic extraction and training processes. The flows of semantic extraction procedures are based on Tsang’s and Wong’s (2002) design