Title:
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Transition-based dependency parsing with heuristic backtracking
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Author:
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Buckman, Jacob; Ballesteros, Miguel; Dyer, Chris
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Abstract:
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Comunicació presentada a Conference on Empirical Methods in Natural Language Processing |
Abstract:
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We introduce a novel approach to the decoding problem in transition-based parsing: heuristic backtracking. This algorithm uses a series of partial parses on the sentence to locate the best candidate parse, using confidence estimates/nof transition decisions as a heuristic to guide the starting points of the search. This allows us to achieve a parse accuracy comparable to beam search, despite using fewer transitions. When used to augment a Stack-LSTM transition-based parser, the parser shows an unlabeled attachment score of up to 93.30% for English and 87.61% for Chinese. |
Abstract:
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Miguel Ballesteros was supported by the European Commission under the contract numbers FP7-/nICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA). |
Subject(s):
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-Lingüística computacional -Tractament del llenguatge natural (Informàtica) |
Rights:
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© ACL, Creative Commons Attribution 4.0 License
http://creativecommons.org/licenses/by/4.0/ |
Document type:
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Conference Object Article - Published version |
Published by:
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ACL (Association for Computational Linguistics)
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