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A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning ...
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Abstract:
Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD ... : 25 pages, 7 figures ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2002.10116 https://dx.doi.org/10.48550/arxiv.2002.10116
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Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool ...
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