Communications of the ACM - Special issue on parallelism
Information-based syntax and semantics: Vol. 1: fundamentals
Information-based syntax and semantics: Vol. 1: fundamentals
Machine translation: how far can it go?
Machine translation: how far can it go?
The connection machine
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Experience, Memory and Reasoning
Experience, Memory and Reasoning
Experiments and prospects of Example-Based Machine Translation
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Memory capacity and sentence processing
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
High performance memory-based translation on IXM2 massively parallel associative memory processor
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Wafer scale integration for massively parallel memory-based reasoning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Massively parallel support for computationally effective recognition queries
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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This paper discusses a radically new scheme of natural language processing called massively parallel memory-based parsing. Most parsing schemes are rule-based or principle-based which involves extensive serial rule application. Thus, it is a time consuming task which requires a few seconds or even a few minutes to complete the parsing of one sentence. Also, the degree of parallelism attained by mapping such a scheme on parallel computers is at most medium, so that the existing scheme can not take advantage of massively parallel computing. The massively parallel memory-based parsing takes a radical departure from the traditional view. It views parsing as a memory-intensive process which can be sped up by massively parallel computing. Although we know of some studies in this direction, we have seen no report regarding implementation strategies on actual massively parallel machines, on performance, or on practicality accessment based on actual data. Thus, this paper focuses on discussion of the feasibility and problems of the approach based on actual massively parallel implementation using real data. The degree of parallelism attained in our model reaches a few thousands, and the performance of a few milliseconds per sentence has been accomplished. In addition, parsing time grows only linearly (or sublincarly) to the length of the input sentences. The experimental results show the approach is promising for real-time parsing and bulk text processing.