pyNeurboParser

Semantic Dependency Parsing (SDP) is the task of recovering sentence-internal predicate–argument relationships for content words in text. This software provides a python wrapper and docker enviorment for the NeurboParser semantic dependency parser library, to enable easier usage.

Prerequites

• Docker Client

Setup

1. Clone the pyNeurboParser Repo
2. Download sdp_models.zip file from releases into the pretrained_models folder.
3. Run the pyNeurboParser container

	docker run --rm -it -v path_to_repo:/data/ -p 5000:5000 abornst/py-neurbo-parser

Usage

• Command Line

In the container run the following command to evaluate the model

python /data/src/SDP_eval.py  --pruner "Path to pruner model" --model "Path to SDP model" --pred "Path to prediction output" --text "Text to be parsed"

• Programatic

    sys.path.append("/data/src/")
    from SDP_eval import semantic_parse

    json_parse = semantic_parse(text, parser_path, pruner_path, data_file, model_path, prediction_path)

Training

1. Run setup (note you only have to do this once)

python /data/src/SDP_setup.py --train_dir "Directory for SDP train data" --test_dir "Directory for SDP test data" --embedding "Glove file"

2. Run train script

python /data/src/SDP_train.py --model "Path to write models" --pred "Path to write predictions" --log "Path to write logs"
--language "Parser Language" --form "Desired SDP formalism"

Demo

An example usage of this wrapper in a flask application can be found in the demo app

References

[1] 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).

[2] 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).

[3] 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).

[4] 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.

[5] 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.