Joint RNN-Based Greedy Parsing and Word Composition
| Autor: | Legrand, Jo��l, Collobert, Ronan |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Neural and Evolutionary Computing (cs.NE) Computation and Language (cs.CL) Machine Learning (cs.LG) |
| DOI: | 10.48550/arxiv.1412.7028 |
| Popis: | This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are jointly trained, and tightly depends on each-other. The composition procedure outputs a vector representation which summarizes syntactically (parsing tags) and semantically (words) sub-trees. Composition and tagging is achieved over continuous (word or tag) representations, and recurrent neural networks. We reach F1 performance on par with well-known existing parsers, while having the advantage of speed, thanks to the greedy nature of the parser. We provide a fully functional implementation of the method described in this paper. Published as a conference paper at ICLR 2015 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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