This is the JAMR Parser, updated for SemEval 2016 Task 8.
JAMR is a semantic parser, generator, and aligner for the Abstract Meaning Representation. The parser and aligner have been updated to include improvements from SemEval 2016 Task 8.
For the generator, see the branch Generator.
We have released hand-alignments for 200 sentences of the AMR corpus.
For the performance of the parser (including for the parser from SemEval 2016), see docs/Parser_Performance.
First checkout the github repository (or download the latest release):
git clone https://github.com/jflanigan/jamr.git
git checkout Semeval-2016
JAMR depends on Scala, Illinois NER system v2.7, tokenization scripts in cdec, and WordNet for the aligner. To manually install them, you can use SDKMAN! and run the following:
sdk install sbt 1.0.2
sdk install scala 2.11.12
To download these dependencies into the subdirectory tools, cd to the jamr repository and run (requires
wget to be installed):
./setup
You should agree to the terms and conditions of the software dependencies before running this script. If you download
them yourself, you will need to change the relevant environment variables in scripts/config.sh. You may need to edit
the Java memory options in the scripts run, sbt, and build.sbt if you get out of memory errors.
Source the config script - you will need to do this before running any of the scripts below:
. scripts/config.sh
Run ./compile to build an uberjar, which will be output to
target/scala-{scala_version}/jamr-assembly-{jamr_version}.jar (the setup script does this for you).
Download and extract model weights models-2016.09.18.tgz into the directory
$JAMR_HOME/models. To parse a file (cased, untokenized, with one sentence per line, no blank lines) with the model trained on
LDC2015E86 data do:
. scripts/config.sh
scripts/PARSE.sh < input_file > output_file 2> output_file.err
The output is AMR format, with some extra fields described in docs/Nodes and Edges
Format and docs/Alignment Format. To run the parser trained
on other datasets (such as LDC2014T12, or the freely downloadable Little
Prince data) source the config scripts config_Semeval-2016_LDC2014T12.sh
or config_Semeval-2016_Little_Prince.sh instead.
To run the rule-based aligner:
. scripts/config.sh
scripts/ALIGN.sh < amr_input_file > output_file
The output of the aligner is described in docs/Alignment Format. The aligner has been updated for SemEval 2016.
To create the hand alignments file, see docs/Hand Alignments.
The following describes how to train and evaluate the parser. There are scripts to train the parser on various datasets, as well as a general train script to train the parser on any AMR dataset. More detailed instructions for training the parser are in docs/Step by Step Training.
To train the parser on LDC data or public AMR Bank data, download the data .tgz file
into to $JAMR_HOME/data/ and run one of the train scripts. The data file and the train script to run for each of the datasets
is listed in the following table:
| Dataset | Date released | Size (# sents) | Script to run | File to move to data/ |
|---|---|---|---|---|
| LDC2015E86 (SemEval 2016 Task 8 data) | August 31, 2015 | 19,572 | scripts/train_LDC2015E86.sh |
LDC2015E86_DEFT_Phase_2_AMR_Annotation_R1.tgz |
| LDC2014T12 | June 16, 2014 | 13,051 | scripts/train_LDC2014T12.sh |
amr_anno_1.0_LDC2014T12.tgz |
| LDC2014E41 | May 30, 2014 | 18,779 | scripts/train_LDC2014E41.sh |
LDC2014E41_DEFT_Phase_1_AMR_Annotation_R4.tgz |
| LDC2013E117 (Proxy only) | October 14, 2013 | 8,219 | scripts/train_LDC2013E117.sh |
LDC2013E117.tgz |
| AMR Bank v1.4 | November 14, 2014 | 1,562 | scripts/train_Little_Prince.sh |
(automatically downloaded) |
For LDC2013E117, LDC2014E41, or LDC2015E86, you will need a license for LDC DEFT project data. The trained model will go into a subdirectory of models/ and the evaulation results will be printed and saved to
models/directory/RESULTS.txt. The performance of the parser on the various datasets is in docs/Parser
Performance.
To train the parser on another dataset, create a config file in scripts/ and
then do:
. scripts/my_config_file.sh
scripts/TRAIN.sh
The trained model will be saved into the $MODEL_DIR specified in the config script, and the results saved in
$MODEL_DIR/RESULTS.txt To run the parser with your trained model, source my_config_file.sh before running
PARSE.sh.
To evaluate a trained model against a gold standard AMR file, do:
. scripts/my_config_file.sh
scripts/EVAL.sh gold_amr_file optional_iteration
The optional_iteration specifies which weight file iteration to use, otherwise stage2-weights is used. The predicted
output will be in models/my_directory/gold_amr_file.parsed-gold-concepts for the parser with oracle concept ID,
models/my_directory/gold_amr_file.parsed for the full pipeline, and the results saved in
models/my_directory/gold_amr_file.results.