This software implements the semantic dependency parsers described in : './NeurboParser' corresponds to the basic model, and './MeurboParser' to the multitask models.
The following software are needed to build the parser:
- A C++ compiler supporting the [C++11 language standard] (https://en.wikipedia.org/wiki/C%2B%2B11)
- Boost libraries (tested with version 1.61.0)
- Eigen (newer versions strongly recommended. tested with development version, changeset 9647:9464b6f3131c)
- CMake (tested with version 3.6.2)
Checking out the project for the first time
git clone https://github.com/Noahs-ARK/NeurboParser.git cd NeurboParser
The first time you clone the repository, you need to initialize the
git submodule update --init
To fetch all the other required libs (except boost):
To build the parser
mkdir -p NeurboParser/build cd NeurboParser/build cmake ..; make -j4 cd ../..
Running/training the parser
Follow the instructions under './semeval2015_data' to prepare the data. You can use the scripts in './NeurboParser' to train/evaluate the parser.
To replicate the results
You will first need to put 100-dimensional pretrained GloVe embedding  under 'embedding/'. Default hyperparameters in the scripts are used in .
The current version of DyNet uses some different strategies to deal with numerical issues than the older version we used in . Based on our experience, we expect the current parser to have slightly better evaluation numbers on benchmark datasets than those described in .
We are still working on adapting the multitask models to use the new version of DyNet.
 Hao Peng, Sam Thomson, and Noah A. Smith. 2017. Deep Multitask Learning for Semantic Dependency Parsing In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
 André F. T. Martins, Miguel B. Almeida, Noah A. Smith. 2013. Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).
 Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP).
 André F. T. Martins and Mariana S. C. Almeida. 2014. Priberam: A Turbo Semantic Parser with Second Order Features. In Proceedubgs of the International Workshop on Semantic Evaluation (SemEval), task 8: Broad-Coverage Semantic Dependency Parsing.
 Mariana S. C. Almeida and André F. T. Martins. 2015. Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data. In Proceedings of International Workshop on Semantic Evaluation (SemEval'15), task 18: Broad Coverage Semantic Dependency Parsing.