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

Quick Start

You can get started and parse a few simple sentences in under five minutes using our AMI image (ami-bdc8d8d7). Launch an EC2 instance (recommended r3.2xlarge at least) with our AMI image. The code in the image is fully complied and ready to run. The ~/amr/ command will quickly parse a few example sentences. The AMI is based on Ubuntu and the user name is ubuntu. The code repository is available here.


Java 8.

Preparing the Repository

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

Pre-trained Models

A pre-trained model is available to download here.


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/
  • Prepare the data: utils/


  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       = {}