Integrating Linguistic Primitives in Learning Context-Dependent Representation
IEEE Transactions on Knowledge and Data Engineering
Automatic discourse structure detection using shallow textual continuity
International Journal of Human-Computer Studies
A Network Analysis Model for Disambiguation of Names in Lists
Computational & Mathematical Organization Theory
Emotion recognition from text using semantic labels and separable mixture models
ACM Transactions on Asian Language Information Processing (TALIP)
Hybrid thematic role processor: symbolic linguistic relations revised by connectionist learning
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Natural language understanding involves the simultaneous consideration of a large number of different sources of information. Traditional methods employed in language analysis have focused on developing powerful formalisms to represent syntactic or semantic structures along with rules for transforming language into these formalisms. However, they make use of only small subsets of knowledge. This article describes how to use the whole range of information through a neurosymbolic architecture which is a hybridization of a symbolic network and subsymbol vectors generated from a connectionist network. Besides initializing the symbolic network with prior knowledge, the subsymbol vectors are used to enhance the system's capability in disambiguation and provide flexibility in sentence understanding. The model captures a diversity of information including word associations, syntactic restrictions, case-role expectations, semantic rules and context. It attains highly interactive processing by representing knowledge in an associative network on which actual semantic inferences are performed. An integrated use of previously analyzed sentences in understanding is another important feature of our model. The model dynamically selects one hypothesis among multiple hypotheses. This notion is supported by three simulations which show the degree of disambiguation relies both on the amount of linguistic rules and the semantic-associative information available to support the inference processes in natural language understanding. Unlike many similar systems, our hybrid system is more sophisticated in tackling language disambiguation problems by using linguistic clues from disparate sources as well as modeling context effects into the sentence analysis. It is potentially more powerful than any systems relying on one processing paradigm