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5th Message Understanding Conference, MUC 1993

作者： Moldovan, Dan Cha, Seungho Chung, Minhwa Gallippi, Tony Hendrickson, Kenneth J. Kim, Jun-Tae Lin, Changhwa Lin, Chinyew Parallel Knowledge Processing Laboratory Department of EE-Systems University of Southern California Los AngelesCA90089-2562 United States

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Design of a clustered multiprocessor for real-time natural language understanding 5

Design of a clustered multiprocessor for real-time natural l...

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5th International parallel processing Symposium, IPPS 1991

作者： DeMara, R.F. Moldovan, D.I. Parallel Knowledge Processing Laboratory Department of Electrical Engineering - Systems University of Southern California Los AngelesCA90089-1115 United States

ISBN: (纸本)0818691670

We have designed a parallel architecture called the Semantic Network Array Processor (SNAP) for Natural Language Understanding (NLU) and other Artificial Intelligence applications. It is capable of executing large marker-passing programs and generating results in real-time. The design features 32 processing clusters with four to five functionally dedicated Digital Signal Processors in each cluster. Processors within clusters share a marker-processing memory while communication between clusters is implemented by a buffered message-passing scheme. Throughout the machine, overlapping groups of multipart memories provide a direct yet visible interconnection network. The result is a low cost, flexible, and observable parallel processor capable of performing NLU operations within subsecond response time. © 1991 IEEE.

关键词： Digital signal processors

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TextNet - A text-based intelligent system

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Natural Language Engineering 1997年第2期3.0卷 171-190页

作者： Harabagiu, Sanda M. Moldovan, Dan I. Parallel Knowledge Processing Laboratory University of Southern California Los Angeles CA 90089-2562 United States Department of Computer Science and Engineering Southern Methodist University Dallas TX 75275-0122 United States

A large collection of texts may be reached through the Internet and this provides a powerful platform from which common-sense knowledge may be gathered. This paper presents a system that contains a core knowledge base structured around WordNet, a lexical database, capable of extracting contextual information from a given input text. Such context information is then used to retrieve other texts from the Internet that relate to that context. When processed by the system, these new texts bring more information that represents an enhanced domain context for the initial text. This is an incremental method for text processing that acquires domain knowledge from other texts. The paper describes the system architecture, its core knowledge base and inference engine, and the acquisition of new knowledge from corpora. © 1997, Cambridge University Press. All rights reserved.

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A marker-passing algorithm for reference resolution

A marker-passing algorithm for reference resolution

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： S. Cha D.I. Moldovan Parallel Knowledge Processing Laboratory University of Southern California Los Angeles CA USA

Reference is an important phenomenon in natural language, and it has been addressed by many researchers. Though a local focus constitutes an important information used in reference resolution, the previous focusing approaches fail to resolve some references due to several problems. The authors present a marker-passing algorithm and some experimental results. Reference resolution is carried out based on the premise that the most active concept which is acceptable syntactically and semantically as a referent is the referent. By defining the activeness of each concept and propagating activeness to related concepts, these problems are avoided. Referability is defined based on constraints and activeness and is used to compute the referent. This model has been implemented on SNAP (Semantic Network Array Processor) simulator, and it shows a 90.2% success rate in various definite references on a set of 100 news articles.

关键词： Computational modeling Laboratories Computer networks

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parallel memory-based parsing on SNAP

Parallel memory-based parsing on SNAP

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International Symposium on parallel processing

作者： M. Chung D. Moldovan Parallel Knowledge Processing Laboratory Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

The paper presents a parallel natural language processing system implemented on a marker-passing parallel AI computer, Semantic Network Array Processor (SNAP). The system uses a memory-based parsing approach in which parsing is viewed as a memory search process. Linguistic information is stored as phrasal patterns in a semantic network knowledge base distributed over the memory of the parallel computer. Parsing is performed by recognizing and linking linguistic patterns that reflect a sentence interpretation. This is achieved via propagating markers over the distributed network. The authors have developed a system capable of processing newswire articles from a particular domain. The paper presents the structure of the system, the memory-based parsing method used, and performance results obtained.< >

关键词： Natural language processing Artificial intelligence parallel processing Concurrent computing Algorithm design and analysis knowledge representation Laboratories knowledge engineering Natural languages Computer networks

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The SNAP-1 parallel AI prototype

The SNAP-1 parallel AI prototype

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Annual International Symposium on Computer Architecture, ISCA

作者： R.F. DeMara D.I. Moldovan Parallel Knowledge Processing Laboratory Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

The Semantic Network Array Processor (SNAP) is a parallel architecture for Artificial Intelligence (AI) applications. We haue implemented a first-generation hardware/soflware prototype called SNAP-1 using Digital Signal Processor chips and ouerlapping groups of multiport memories. The design features 32 processing clusters with four to five functionally dedicated Digital Signal Processors in each cluster. Processors within clusters share a marker-processing memo y while communication between clusters is implemented by a buffered messagepassing scheme.

关键词： Artificial intelligence Prototypes Hardware Permission parallel processing Digital signal processors Application software Computer architecture

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Design of a clustered multiprocessor for real-time natural language understanding

Design of a clustered multiprocessor for real-time natural l...

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International Symposium on parallel processing

作者： R.F. DeMara D.I. Moldovan Parallel Knowledge Processing Laboratory Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

The authors have designed a parallel architecture called the semantic network array processor (SNAP) for natural language understanding (NLU) and other artificial intelligence applications. It is capable of executing large marker-passing programs and generating results in real-time. The design features 32 processing clusters with four to five functionally dedicated digital signal processors in each cluster. Processors within clusters share a marker-processing memory while communication between clusters is implemented by a buffered message-passing scheme. Throughout the machine, overlapping groups of multiport memories provide a direct yet visible interconnection network. The result is a low cost, flexible, and observable parallel processor capable of performing NLU operations within subsecond response time.< >

关键词： Natural languages Artificial intelligence Application software Hardware Control systems Sun Real time systems Laboratories Electronic mail parallel architectures

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