| 1. |
- de Lhoneux, Miryam, 1990-, et al.
(författare)
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From raw text to Universal Dependencies : look, no tags!
- 2017
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Ingår i: Proceedings of the CoNLL 2017 Shared Task. - Vancouver, Canada : Association for Computational Linguistics. - 9781945626708 ; , s. 207-217
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Konferensbidrag (refereegranskat)abstract
- We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies. Our system is a simple pipeline consisting of two components. The first performs joint word and sentence segmentation on raw text; the second predicts dependency trees from raw words. The parser bypasses the need for part-of-speech tagging, but uses word embeddings based on universal tag distributions. We achieved a macroaveraged LAS F1 of 65.11 in the official test run and obtained the 2nd best result for sentence segmentation with a score of 89.03. After fixing two bugs, we obtained an unofficial LAS F1 of 70.49.
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| 2. |
- de Lhoneux, Miryam, 1990-, et al.
(författare)
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Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle
- 2017
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Ingår i: IWPT 2017 15th International Conference on Parsing Technologies. - Pisa, Italy : Association for Computational Linguistics. - 9781945626739 ; , s. 99-104
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Konferensbidrag (refereegranskat)abstract
- We extend the arc-hybrid transition system for dependency parsing with a SWAP transition that enables reordering of the words and construction of non-projective trees. Although this extension potentially breaks the arc-decomposability of the transition system, we show that the existing dynamic oracle can be modified and combined with a static oracle for the SWAP transition. Experiments on five languages with different degrees of non-projectivity show that the new system gives competitive accuracy and is significantly better than a system trained with a purely static oracle.
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| 3. |
- de Lhoneux, Miryam, 1990-, et al.
(författare)
-
Old School vs. New School : Comparing Transition-Based Parsers with and without Neural Network Enhancement
- 2017
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Ingår i: <em>Proceedings of the 15th Treebanks and Linguistic Theories Workshop (TLT)</em>. ; , s. 99-110
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Konferensbidrag (refereegranskat)abstract
- In this paper, we attempt a comparison between "new school" transition-based parsers that use neural networks and their classical "old school" coun-terpart. We carry out experiments on treebanks from the Universal Depen-dencies project. To facilitate the comparison and analysis of results, we onlywork on a subset of those treebanks. However, we carefully select this sub-set in the hope to have results that are representative for the whole set oftreebanks. We select two parsers that are hopefully representative of the twoschools; MaltParser and UDPipe and we look at the impact of training sizeon the two models. We hypothesize that neural network enhanced modelshave a steeper learning curve with increased training size. We observe, how-ever, that, contrary to expectations, neural network enhanced models needonly a small amount of training data to outperform the classical models butthe learning curves of both models increase at a similar pace after that. Wecarry out an error analysis on the development sets parsed by the two sys-tems and observe that overall MaltParser suffers more than UDPipe fromlonger dependencies. We observe that MaltParser is only marginally betterthan UDPipe on a restricted set of short dependencies.
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