J.-C. Chappelier and M. Rajman. Monte-Carlo sampling for NP-hard maximization problems in the framework of weighted parsing. In D.N. Christodoulakis, editor, Natural Language Processing -- NLP 2000, number 1835 in Lecture Notes in Artificial Intelligence, pages 106-- 117. Springer, 2000. Home/Search Document Details and Download
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Parsing with the Shortest Derivation - Bod (2000) (2 citations) (Correct)
....so as to maintain the subtree s internal structure and probability. These rules are used to create a derivation forest for a sentence, and the most probable parse is computed by sampling a sufficiently large number of random derivations from the forest ( Monte Carlo disambiguation , see Bod 1998; Chappelier Rajman 2000). While this technique has been successfully applied to parsing the ATIS portion in the Penn Treebank (Marcus et al. 1993) it is extremely time consuming. This is mainly because the number of random derivations that should be sampled to reliably estimate the most probable parse increases ....
Chappelier, J. and M. Rajman, 2000. "Monte Carlo Sampling for NP-hard Maximization Problems in the Framework of Weighted Parsing", in Natural Language Processing -- NLP 2000, Lecture Notes in Artificial Intelligence 1835 , D. Christodoulakis (ed.), 2000, 106-117.
An Improved Parser for Data-Oriented Lexical-Functional Analysis - Bod (2000) (1 citation) (Correct)
....is accomplished by computing a large number of random derivations from the chart and by selecting the analysis which results most often from these derivations. This technique is known as Monte Carlo disambiguation and has been extensively described in the literature (e.g. Bod 1993, 1998; Chappelier Rajman 2000; Goodman 1998; Hoogweg 2000) Sampling a random derivation from the chart consists of choosing at random one of the fragments from the set of composable fragments at every labeled chart entry (where the random choices at each chart entry are based on the probabilities of the fragments) The ....
J. Chappelier and M. Rajman, 2000. "Monte Carlo Sampling for NP-hard Maximization Problems in the Framework of Weighted Parsing", in NLP 2000, Lecture Notes in Artificial Intelligence 1835, 106-117.
Polynomial Tree Substitution Grammars: an efficient.. - Chappelier, Rajman Self-citation (Chappelier Rajman) (Correct)
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J.-C. Chappelier and M. Rajman. Monte-Carlo sampling for NP-hard maximization problems in the framework of weighted parsing. In D.N. Christodoulakis, editor, Natural Language Processing -- NLP 2000, number 1835 in Lecture Notes in Artificial Intelligence, pages 106-- 117. Springer, 2000.
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