IMSnPars

Graph-based and transition-based neural dependency parsers

IMSnPars

Typ

Tool

Autor

Agnieszka Faleńska

Beschreibung

IMS Neural Dependency Parser is an extended re-implementation of the transition- and graph-based parsers described in Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

Referenz

The parser was developed for the paper:

Agnieszka Falenska, Jonas Kuhn. The (Non-) Utility of Structural Features in BiLSTM-based Dependency Parsers. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

Later it was extended with multi-task training and deep contextualized embeddings for the paper:

Agnieszka Falenska, Anders Björkelund, and Jonas Kuhn. Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era. Proceedings of the 16th International Conference on Parsing Technologies.

Download

See IMSnPars for the code and description of the parameters.

 

Kontakt IMS

Pfaffenwaldring 5 b, 70569 Stuttgart

 

Webmaster des IMS

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