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Higher-order Derivatives of Weighted Finite-state Machines ...
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Efficient computation of expectations under spanning tree distributions ...
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Higher-order Derivatives of Weighted Finite-state Machines ...
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| 8 |
On Finding the K-best Non-projective Dependency Trees
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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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| 9 |
Higher-order Derivatives of Weighted Finite-state Machines
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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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| 10 |
Efficient computation of expectations under spanning tree distributions
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In: Transactions of the Association for Computational Linguistics, 9 (2021)
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| 11 |
Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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| 12 |
Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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| 14 |
If beam search is the answer, what was the question?
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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| 15 |
A Joint Model of Orthography and Morphological Segmentation
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Cotterell, Ryan; Vieira, Tim; Schütze, Hinrich. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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| 16 |
Grammarless parsing for joint inference
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Abstract:
Many NLP tasks interact with syntax. The presence of a named entity span, for example, is often a clear indicator of a noun phrase in the parse tree, while a span in the syntax can help indicate the lack of a named entity in the spans that cross it. For these types of problems joint inference offers a better solution than a pipelined approach, and yet large joint models are rarely pursued. In this paper we argue this is due in part to the absence of a general framework for joint inference which can efficiently represent syntactic structure. We propose an alternative and novel method in which constituency parse constraints are imposed on the model via combinatorial factors in a Markov random field, guaranteeing that a variable configuration forms a valid tree. We apply this approach to jointly predicting parse and named entity structure, for which we introduce a zero-order semi-CRF named entity recognizer which also relies on a combinatorial factor. At the junction between these two models, soft constraints coordinate between syntactic constituents and named entity spans, providing an additional layer of flexibility on how these models interact. With this architecture we achieve the best-reported results on both CRF-based parsing and named entity recognition on sections of the OntoNotes corpus, and outperform state-of-the-art parsers on an NP-identification task, while remaining asymptotically faster than traditional grammar-based parsers. ; 16 page(s)
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URL: http://hdl.handle.net/1959.14/216833
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