Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages

Xiang Yu; Ngoc Thang Vu

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arXiv · 1705.10814

Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages

Xiang Yu·Ngoc Thang Vu

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Abstract

We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et al., 2015) by a margin of 3% on average.

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Xiang Yu, Ngoc Thang Vu. 2017-05-30. Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages. https://arxiv.org/abs/1705.10814

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