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Command-line evaluation tools for named entity linking and (cross-document) coreference resolution
Python command-line evaluation scripts for TAC <http://www.nist.gov/tac/>
__
entity linking and related wikification, named entity disambiguation, and
within- and cross-document coreference tasks.
|version| |licence| |py-versions|
|issues| |build| |docs| |coverage|
It aims for fast and flexible coreference resolution and
sophisticated named entity recognition evaluation, such as partial scores
for partial overlap between gold and system mentions. CEAF, in particular, is
much faster to calculate here than in the CoNLL-11/12 scorer <https://github.com/conll/reference-coreference-scorers>
__. It boasts features
such as configurable metrics; accounting for or ignoring cross-document
coreference (see the evaluate --by-doc
flag); plotting to compare
evaluation by system, measure and corpus subset; and bootstrap-based confidence
interval calculation for document-wise evaluation metrics.
Requires that python
(2.7, with Py3k support experimental/partial)
be installed on your system with numpy
(and preferably scipy
for
fast CEAF calculation) and joblib
. matplotlib
is required for
the plot-systems
command.
See a list of commands with:
.. code:: bash
./nel --help
Or install onto your Python path (e.g. with
pip install git+https://github.com/wikilinks/neleval
) then
.. code:: bash
python -m neleval --help
.. code:: bash
./scripts/run_tac14_evaluation.sh \
/path/to/gold.xml \ # TAC14 gold standard queries/mentions
/path/to/gold.tab \ # TAC14 gold standard link and nil annotations
/system/output/directory \ # directory containing (only) TAC14 system output files
/script/output/directory \ # directory to which results are written
number_of_jobs # number of jobs for parallel mode
Each file in in the system output directory is scored against gold.tab.
Similar facility is available for TAC-KBP'15 EDL.
See the documentation <https://neleval.readthedocs.io>
__ for more
details.
.. |py-versions| image:: https://img.shields.io/pypi/pyversions/neleval.svg :alt: Python versions supported
.. |version| image:: https://badge.fury.io/py/neleval.svg :alt: Latest version on PyPi :target: https://badge.fury.io/py/neleval
.. |build| image:: https://travis-ci.org/wikilinks/neleval.svg?branch=master :alt: Travis CI build status :scale: 100% :target: https://travis-ci.org/wikilinks/neleval
.. |issues| image:: https://img.shields.io/github/issues/wikilinks/neleval.svg :alt: Issue tracker :target: https://github.com/wikilinks/neleval
.. |coverage| image:: https://coveralls.io/repos/github/wikilinks/neleval/badge.svg :alt: Test coverage :target: https://coveralls.io/github/wikilinks/neleval
.. |docs| image:: https://readthedocs.org/projects/neleval/badge/?version=latest :alt: Documentation Status :scale: 100% :target: https://neleval.readthedocs.io/en/latest/?badge=latest
.. |licence| image:: https://img.shields.io/badge/Licence-Apache%202.0-blue.svg :target: https://opensource.org/licenses/Apache-2.0
FAQs
Command-line evaluation tools for named entity linking and (cross-document) coreference resolution
We found that neleval demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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