490 research outputs found
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
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.Comment: Accepted in ACL 2017 (Short
A Sub-Character Architecture for Korean Language Processing
We introduce a novel sub-character architecture that exploits a unique
compositional structure of the Korean language. Our method decomposes each
character into a small set of primitive phonetic units called jamo letters from
which character- and word-level representations are induced. The jamo letters
divulge syntactic and semantic information that is difficult to access with
conventional character-level units. They greatly alleviate the data sparsity
problem, reducing the observation space to 1.6% of the original while
increasing accuracy in our experiments. We apply our architecture to dependency
parsing and achieve dramatic improvement over strong lexical baselines.Comment: EMNLP 201
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