We propose a Korean dependency parsing system that can learn the relationships between Korean words from the Treebank corpus and a large raw corpus. We first refine the training dataset to better represent the relationship using a different POS tagging granularity type. We also introduce lexical information and propose an almost fully lexicalized probabilistic model with case frames automatically extracted from a very large raw corpus. We evaluate and compare systems with and without POS granularity refinement and case frames. The proposed lexicalized method outperforms not only the baseline systems but also a state-of-the-art supervised dependency parser.
|出版ステータス||Published - 2013|
|イベント||13th International Conference on Parsing Technologies, IWPT 2013 - Nara, Japan|
継続期間: 2013 11月 27 → 2013 11月 29
|Conference||13th International Conference on Parsing Technologies, IWPT 2013|
|Period||13/11/27 → 13/11/29|
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