Abstract
The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in the framework of weighted parsing, and (2) to show how such sampling techniques can be efficiently implemented with an explicit control of the error probability. We provide an algorithm to compute the local sampling probability distribution that guarantee that the global sampling probability indeed corresponds to the aimed theoretical score. The proposed sampling strategy significantly differs from existing methods, showing by the same way the bias induced by these methods.
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Chappelier, JC., Rajman, M. (2000). Monte-Carlo Sampling for NP-Hard Maximization Problems in the Framework of Weighted Parsing. In: Christodoulakis, D.N. (eds) Natural Language Processing — NLP 2000. NLP 2000. Lecture Notes in Computer Science(), vol 1835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45154-4_10
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DOI: https://doi.org/10.1007/3-540-45154-4_10
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