Sequence Tagging for Fast Dependency Parsing
- Michalina Strzyz
- David Vilares 1
- Carlos Gómez-Rodríguez 1
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1
Universidade da Coruña
info
- Alberto Alvarellos González (ed. lit.)
- José Joaquim de Moura Ramos (ed. lit.)
- Beatriz Botana Barreiro (ed. lit.)
- Javier Pereira Loureiro (ed. lit.)
- Manuel F. González Penedo (ed. lit.)
Editorial: MDPI
ISBN: 978-3-03921-444-0, 978-3-03921-443-3
Año de publicación: 2019
Congreso: XoveTIC (2. 2019. A Coruña)
Tipo: Aportación congreso
Resumen
Dependency parsing has been built upon the idea of using parsing methods based on shift-reduce or graph-based algorithms in order to identify binary dependency relations between the words in a sentence. In this study we adopt a radically different approach and cast full dependency parsing as a pure sequence tagging task. In particular, we apply a linearization function to the tree that results in an output label for each token that conveys information about the word’s dependency relations. We then follow a supervised strategy and train a bidirectional long short-term memory network to learn to predict such linearized trees. Contrary to the previous studies attempting this, the results show that this approach not only leads to accurate but also fast dependency parsing. Furthermore, we obtain even faster and more accurate parsers by recasting the problem as multitask learning, with a twofold objective: to reduce the output vocabulary and also to exploit hidden patterns coming from a second parsing paradigm (constituent grammars) when used as an auxiliary task.