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gregdurrett / berkeley-entity

The Berkeley Entity Resolution System jointly solves the problems of named entity recognition, coreference resolution, and entity linking with a feature-rich discriminative model.
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berkeley-entity

The Berkeley Entity Resolution System jointly solves the problems of named entity recognition, coreference resolution, and entity linking with a feature-rich discriminative model.

Preamble

The Berkeley Entity Resolution System is described in:

"A Joint Model for Entity Analysis: Coreference, Typing, and Linking" Greg Durrett and Dan Klein. TACL 2014.

The coreference portion is described in:

"Easy Victories and Uphill Battles in Coreference Resolution." Greg Durrett and Dan Klein. EMNLP 2013.

See http://www.eecs.berkeley.edu/~gdurrett/ for papers and BibTeX.

Questions? Bugs? Email me at gdurrett@eecs.berkeley.edu

License

Copyright (c) 2013-2015 Greg Durrett. All Rights Reserved.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/

Setup

Models

Models are not included in GitHub due to their large size. Download the latest models from http://nlp.cs.berkeley.edu/projects/entity.shtml

Datasets

See the CoNLL 2012 shared task page for more information about the data formats. All of our files (input and output) follow this standard; when we have subsets of annotations, the corresponding columns are simply left blank (i.e. no coreference chunks or NER chunks, vacuous trees, etc.). Entity links are included in a standoff file so that we avoid modifying these files: they are presented as an extra column with the same specification as NER chunks, with the exception that they can overlap.

The system generally takes directories for input and outputs single files with all documents concatenated. Note that a directory can contain a single file of this form. For training, files are required to have auto_conll and gold_conll suffixes as appropriate; for testing, you can filter the documents to read with -docSuffix.

Flattened directories of CoNLL files can be produced from the CoNLL shared task data as follows:

find . -path .*conll | while read file; do
  cp $file path/to/flattened/directory
done

We also require number and gender data produced by Shane Bergsma and Dekang Lin in "Bootstrapping Path-Based Pronoun Resolution" (default path the system expects this data at: data/gender.data) and [Brown clusters] (http://people.csail.mit.edu/maestro/papers/bllip-clusters.gz) (default path: data/bllip-clusters). pull-datasets.sh should pull these datasets for you and put them in the appropriate locations.

CoNLL Scorer

Available at https://code.google.com/p/reference-coreference-scorers/

There will be three things in the download: scorer.pl, CorScorer.pm, and a directory called Algorithm. Put Algorithm and CorScorer.pl in the directory you run the jar from, or in lib/ under that directory. This way they'll be located for scoring. scorer.pl can go anywhere as long as you pass in the appropriate path with -conllEvalScriptPath; the system expects it at scorer/v7/scorer.pl.

Again, pull-datasets.sh will do all this for you.

Note that all results in the paper come from version 7 of the CoNLL scorer. Other versions of the scorer may return different results.

Running the system

The main class is edu.berkeley.nlp.entity.Driver The running of the system is documented more thoroughly there. It supports running pretrained models on raw text as well as training and evaluating new models.

An example run on new data is included in run-test.sh

Note that this example runs purely from raw text and follows the CoNLL annotation standards. Because the CoNLL dataset does not contain supervised entity linking data, the entity linking component of the model does not give the performance indicated in the paper. If you're particularly interested in entity linking, you should pre-extract mentions from your dataset according to the ACE standard and use the ACE version of the model.

A trained model includes not just feature specifications and weights for the joint model, but also trained coarse models for coreference and NER.

To reproduce CoNLL results, run:

java -Xmx8g -jar berkeley-entity-1.0.jar ++config/base.conf -execDir scratch -mode PREDICT_EVALUATE -testPath data/conll-2012-en/test\
  -modelPath "models/joint-onto.ser.gz" -wikipediaPath "models/wiki-db-onto.ser.gz" \
  -docSuffix auto_conll

To reproduce ACE results, run with:

java -Xmx4g -jar berkeley-entity-1.0.jar ++config/base.conf -execDir scratch -mode PREDICT_EVALUATE_ACE -testPath data/ace05/test \
  -modelPath "models/joint-ace.ser.gz" -wikipediaPath "models/wiki-db-ace.ser.gz" \
  -doConllPostprocessing false -useGoldMentions -wikiGoldPath data/ace05/ace05-all-conll-wiki

Note that this requires the ACE data to be in the CoNLL standard with standoff Wikipedia annotations in ace05-all-conll-wiki. This whole process is sensitive to tokenization and sentence-splitting. If you're interested in reproducing these results, please contact me.

Preprocessing

The system is runnable from raw text as input. It runs a sentence splitter (Gillick, 2009), tokenizer (Penn Treebank), and parser (Berkeley parser), or a subset of these. See edu.berkeley.nlp.entity.preprocess.PreprocessingDriver for more information about these tools and command line options. See run-test.sh for an example usage.

Training

The system expects automatic annotations in files ending with auto_conll (i.e. parses) and gold annotations (i.e. coref and NER) in gold_conll files. Currently the OntoNotes version of the system cannot take gold entity links as supervision; email me if you are interested in such functionality.

To train a CoNLL model, run:

java -Xmx47g -jar berkeley-entity-1.0.jar ++config/base.conf -execDir scratch -mode TRAIN_EVALUATE \
  -trainPath data/conll-2012-en/train -testPath data/conll-2012-en/test -modelPath models/joint-new-onto.ser.gz \
  -wikipediaPath models/cached/wiki-db-onto.ser.gz \
  -pruningStrategy build:models/cached/corefpruner-onto.ser.gz:-5:5 \
  -nerPruningStrategy build:models/cached/nerpruner-onto.ser.gz:-9:5 \
  -numItrs 30

To train an ACE model run:

java -Xmx35g -jar berkeley-entity-1.0.jar ++config/base.conf -execDir scratch -mode TRAIN_EVALUATE_ACE \
  -trainPath data/ace05/train -testPath data/ace05/test -modelPath models/cached/joint-new-ace.ser.gz \
  -wikipediaPath models/wiki-db-ace.ser.gz \
  -pruningStrategy build:models/cached/corefpruner-ace.ser.gz:-5:5 \
  -doConllPostprocessing false -useGoldMentions -wikiGoldPath data/ace05/ace05-all-conll-wiki \
  -lossFcn customLoss-1-1-1 \
  -numItrs 20

Note that because ACE NER mentions are synchronous with the coreference mentions, the NER layer is much simpler (isolated random variables rather than a sequence model) and so NER pruning is not necessary here.

Building from source

The easiest way to build is with SBT: https://github.com/harrah/xsbt/wiki/Getting-Started-Setup

then run

sbt assembly

which will compile everything and build a runnable jar.

You can also import it into Eclipse and use the Scala IDE plug-in for Eclipse http://scala-ide.org

Adding features

Features can be specified on the command line and are instantiated in a few different places.

Coreference: edu.berkeley.nlp.entity.coref.PairwiseIndexingFeaturizerJoint, control with -pairwiseFeats

NER: edu.berkeley.nlp.entity.ner.NerFeaturizer, control with -nerFeatureSet

Linking: edu.berkeley.nlp.entity.wiki.QueryChoiceComputer

Joint: edu.berkeley.nlp.entity.joint.JointFeaturizerShared, control with -corefNerFeatures, -wikiNerFeatures, -corefWikiFeatures

Note that turning off all of the joint features by passing in empty strings to each yields the results for independent models (INDEP results from the TACL 2014 paper).

The methods to instantiate features are extensible. Additional information sources can either be passed to the featurizers or accessed in a static fashion.

Troubleshooting

Calling the coreference scorer (in TRAIN_EVALUATE mode) may cause an out-of-memory error because under the hood, Java forks the process and if you're running with a lot of memory, it may crash. You can use the coreference system in COREF_PREDICT or COREF_TRAIN_PREDICT and then evaluate separately to avoid this.