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RBGParser v1.1

This version improves parsing speed using the hash kernel (see [4]) and by optimizing the code. We also improved the unlabeled attachment score (UAS) slightly and labeled attachment score (LAS) significantly.

• feature index lookup: use hash kernel (i.e. ignoring collisions) instead of a look-up table
• dependency labels: now use a complete set of first-order features; will consider adding rich features later
• online update method: a slightly modified version
• optimized feature cache at code level
• now can prune low-frequent labels, words, etc.

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About and Contact

This project is developed at Natural Language Processing group in MIT. It contains a Java implementation of a syntactic dependency parser with tensor decomposition and greedy decoding, described in [1,2,3].

This project is implemented by Tao Lei (taolei [at] csail.mit.edu) and Yuan Zhang (yuanzh [at] csail.mit.edu).

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References

[1] Tao Lei, Yu Xin, Yuan Zhang, Regina Barzilay and Tommi Jaakkola. Low-Rank Tensors for Scoring Dependency Structures. ACL 2014. PDF

[2] Yuan Zhang, Tao Lei, Regina Barzilay, Tommi Jaakkola and Amir Globerson. Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees. ACL 2014. PDF

[3] Yuan Zhang*, Tao Lei*, Regina Barzilay and Tommi Jaakkola. Greed is Good if Randomized: New Inference for Dependency Parsing. EMNLP 2014. PDF

[4] Bernd Bohnet. Very High Accuracy and Fast Dependency Parsing is not a Contradiction. The 23rd International Conference on Computational Linguistics. COLING 2010. PDF