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Hits 1 – 10 of 10
1
Higher-order Derivatives of Weighted Finite-state Machines ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Cotterell, Ryan
;
Vieira, Tim
. - : Underline Science Inc., 2021
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2
On Finding the K-best Non-projective Dependency Trees ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Cotterell, Ryan
;
Vieira, Tim
. - : Underline Science Inc., 2021
BASE
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3
On Finding the K-best Non-projective Dependency Trees ...
Zmigrod, Ran
;
Vieira, Tim
;
Cotterell, Ryan
. - : ETH Zurich, 2021
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4
Efficient computation of expectations under spanning tree distributions ...
Zmigrod, Ran
;
Vieira, Tim
;
Cotterell, Ryan
. - : ETH Zurich, 2021
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5
Higher-order Derivatives of Weighted Finite-state Machines ...
Zmigrod, Ran
;
Vieira, Tim
;
Cotterell, Ryan
. - : ETH Zurich, 2021
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6
On Finding the K-best Non-projective Dependency Trees
Zmigrod, Ran
;
Cotterell, Ryan
;
Vieira, Tim
In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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7
Higher-order Derivatives of Weighted Finite-state Machines
Zmigrod, Ran
;
Cotterell, Ryan
;
Vieira, Tim
In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (2021)
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8
Efficient computation of expectations under spanning tree distributions
Cotterell, Ryan
;
Zmigrod, Ran
;
Vieira, Tim
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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9
Efficient Sampling of Dependency Structure
Vieira, Tim
;
Zmigrod, Ran
;
Cotterell, Ryan
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Abstract:
Probabilistic distributions over spanning trees in directed graphs are a fundamental model of dependency structure in natural language processing, syntactic dependency trees. In NLP, dependency trees often have an additional root constraint: only one edge may emanate from the root. However, no sampling algorithm has been presented in the literature to account for this additional constraint. In this paper, we adapt two spanning tree sampling algorithms to faithfully sample dependency trees from a graph subject to the root constraint. Wilson (1996(’s sampling algorithm has a running time of O(H) where H is the mean hitting time of the graph. Colbourn (1996)’s sampling algorithm has a running time of O(N3), which is often greater than the mean hitting time of a directed graph. Additionally, we build upon Colbourn’s algorithm and present a novel extension that can sample K trees without replacement in O(K N3 + K2 N) time. To the best of our knowledge, no algorithm has been given for sampling spanning trees without replacement from a directed graph.
URL:
https://hdl.handle.net/20.500.11850/518990
https://doi.org/10.3929/ethz-b-000518990
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10
Please Mind the Root: Decoding Arborescences for Dependency Parsing
Vieira, Tim
;
Cotterell, Ryan
;
Zmigrod, Ran
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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