时间和空间效率加权扣除

IF 4.2 1区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jason Eisner
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引用次数: 1

摘要

许多NLP算法都是用演绎系统来描述的。非加权扣除允许通用前向链执行策略。然而,对于加权推导,有效的执行应该只在每个项收敛之后才传播其权重。这意味着以拓扑排序的顺序访问项目(如动态规划)。在物化图上拓扑排序速度快;不幸的是,物化图形将占用额外的空间。是否存在一种一般的加权演绎策略,对于每一个非循环演绎系统和每一个输入,只比一般的非加权演绎使用一个常数因子更多的时间和空间?在回顾了过去的策略之后,我们结合Goodman(1999)和Kahn(1962)的观点来肯定地回答这个问题。我们也在Tarjan(1972)的基础上对循环演绎系统进行了扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-and-Space-Efficient Weighted Deduction
Abstract Many NLP algorithms have been described in terms of deduction systems. Unweighted deduction allows a generic forward-chaining execution strategy. For weighted deduction, however, efficient execution should propagate the weight of each item only after it has converged. This means visiting the items in topologically sorted order (as in dynamic programming). Toposorting is fast on a materialized graph; unfortunately, materializing the graph would take extra space. Is there a generic weighted deduction strategy which, for every acyclic deduction system and every input, uses only a constant factor more time and space than generic unweighted deduction? After reviewing past strategies, we answer this question in the affirmative by combining ideas of Goodman (1999) and Kahn (1962). We also give an extension to cyclic deduction systems, based on Tarjan (1972).
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来源期刊
CiteScore
32.60
自引率
4.60%
发文量
58
审稿时长
8 weeks
期刊介绍: The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.
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