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Cornell AMR Semantic Parser (Artzi et al., EMNLP 2015)

Home Page: http://yoavartzi.com/amr

License: GNU General Public License v2.0

Java 49.99% Shell 1.96% PHP 0.69% C++ 24.59% Lex 2.21% Python 5.40% Makefile 0.20% C 0.15% Scala 13.97% Perl 0.69% NASL 0.12% Pawn 0.02%
amr abstract-meaning-representation semantic-parser natural-language-processing machine-learning

amr's Introduction

Cornell AMR Semantic Parser

Requirements

Java 8.

Preparing the Repository

  • Get all required resources: ./getres.sh (form the root of the repository)
  • Compile: ant dist

Pre-trained Models

A pre-trained model is available to download here.

Parsing

Given a file sentences.txt, which contains a sentence on each line, and a model file amr.sp, both located in the root of the repository:

java -Xmx8g -jar dist/amr-1.0.jar parse rootDir=`pwd` modelFile=`pwd`/amr.sp sentences=`pwd`/sentences.txt

The output files will be in experiments/parse/logs. To see the full set of options (including increasing the logging level), run:

java -jar dist/amr-1.0.jar

Preparing the data (required only for training and testing)

To re-create our experiments, obtain the AMR Bank release 1.0 (LDC2014T12) form LDC. Extract the corpus to the directory corpus/amr_anno_1.0.

Then run the following:

  • Compile the code: ant dist
  • Prepare the environment: utils/config.sh
  • Prepare the data: utils/prepdata-ldc.sh

Attribution

@InProceedings{artzi-lee-zettlemoyer:2015:EMNLP,
  author    = {Artzi, Yoav  and  Lee, Kenton  and  Zettlemoyer, Luke},
  title     = {Broad-coverage CCG Semantic Parsing with AMR},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2015},
  address   = {Lisbon, Portugal},
  publisher = {Association for Computational Linguistics},
  pages     = {1699--1710},
  url       = {http://aclweb.org/anthology/D15-1198}
}

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