Multilingual Dependency Learning: Exploiting Rich Features for Tagging Syntactic and Semantic Dependencies

By Zhao Hai, Wenliang Chen, Jun'ichi Kazama, Kiyotaka Uchimoto, Kentaro Torisawa. 2009

72 citations recorded by the source corpus.

This paper describes our system about multilingual syntactic and semantic dependency parsing for our participation in the joint task of CoNLL-2009 shared tasks. Our system uses rich features and incorporates various integration technologies. The system is evaluated on in-domain and out-of-domain evaluation data of closed challenge of joint task. For in-domain evaluation, our system ranks the second for the average macro labeled F1 of all seven languages, 82.52% (only about 0.1% worse than the best system), and the first for English with macro labeled F1 87.69%. And for out-of-domain evaluation, our system also achieves the second for average score of all three languages.

Subfields: Artificial Intelligence

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Citations in the corpus

I Know What You Want: Semantic Learning for Text Comprehension (2018, methodology): “The pioneering work on building an end-to-end neural system was presented by Zhou and Xu (2015), applying an 8 layered LSTM model, which takes only original text information as input feature without using any syntactic knowledge, outperforming the previous state-of-the-art sys-tem.”
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling (2017, methodology): “Similarly to the span-based model of Zhou and Xu (2015) we use bidirectional LSTMs to encode sentences and rely on their states when predicting arguments of each predicate.”
High-Order Low-Rank Tensors for Semantic Role Labeling (2015, methodology): “We also compare our results against the best performing system on this task (Zhao et al., 2009a).”