Online semantic extraction by backpropagation neural network with various syntactic structure representations

Heidi H. T. Yeung

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

An online intelligent system is proposed to extract the semantics by applying a 3-layer backpropagation neural network to classify the encoded syntactic structures into corresponding semantic types. Recursive Auto-Association Memories (RAAM), sequential RAAM, and tensor product representation with nonlinear compression are the three connectionist-based encoders to represent the syntactic structures. The proposed system is implemented with JAVA which is an object-oriented and multiplatform programming language. It is observed that the main program, NLP includes all procedures in the semantic extraction and training processes.
Original languageEnglish
Title of host publicationProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-04): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
PublisherAmerican Association for Artificial Intelligence
Pages1042-1043
Publication statusPublished - 2004
Externally publishedYes
Event19th National Conference on Artificial Intelligence, AAAI-2004, 6th Innovative Applications of Artificial Intelligence Conference, IAAI 2004 - San Jose, CA, United States
Duration: 25 Jul 200429 Jul 2004

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Conference

Conference19th National Conference on Artificial Intelligence, AAAI-2004, 6th Innovative Applications of Artificial Intelligence Conference, IAAI 2004
PlaceUnited States
CitySan Jose, CA
Period25/07/0429/07/04

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