Abstract
Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree. Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved by adding additional non-monotonic transitions that permit the parser to "repair" earlier parsing mistakes by "over-writing" earlier parsing decisions. This increases the size of the set of complete parse trees that each partial parse state can derive, enabling such a parser to escape the "garden paths" that can trap monotonic greedy transition-based dependency parsers. We describe a new set of non-monotonic transitions that permits a partial parse state to derive a larger set of completed parse trees than previous work, which allows our parser to escape from a larger set of garden paths. A parser with our new nonmonotonic transition system has 91.85% directed attachment accuracy, an improvement of 0.6% over a comparable parser using the standard monotonic arc-eager transitions.
Original language | English |
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Title of host publication | Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 |
Subtitle of host publication | Conference Proceedings |
Place of Publication | Red Hook, NY |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1373-1378 |
Number of pages | 6 |
ISBN (Electronic) | 9781941643327 |
DOIs | |
Publication status | Published - 2015 |
Event | Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal Duration: 17 Sept 2015 → 21 Sept 2015 |
Other
Other | Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 17/09/15 → 21/09/15 |