This implementation is based on the cnn library for this software to function. The paper is "Shift-Reduce Constituent Parsing with Neural Lookahead Features".
mkdir build
cd build
cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen
make
You can get the constituent hierarchy by scripts/conhier_s.py for s-type constituent hierarchy and scripts/conhier_e.py for e-type constituent hierachy
./scripts/conhier_s.py [training data in bracketed format] > [s-type training data]
./scripts/conhier_e.py [training data in bracketed format] > [e-type training data]
./scripts/conhier_s.py [development data in bracketed format] > [s-type development data]
./scripts/conhier_e.py [development data in bracketed format] > [e-type development data]
./scripts/conhier_s.py [test data in bracketed format] > [s-type test data]
./scripts/conhier_e.py [test data in bracketed format] > [e-type test data]
The directory data contains the related data.
./build/impl/s-hierarchy-trainer train.s dev.s
./build/impl/e-hierarchy-trainer train.e dev.e
./build/impl/s-hierarchy-trainer train.s test.s [s_model]
./build/impl/e-hierarchy-decoder train.e test.e [e_model]
It will automatically generate the output file test.sOUT and test.eOUT, respectively, which then can be used as extra features on the lookahead implementation of ZPar
./scripts/combine.py [.sOUT] [.eOUT] > [extra feature]
You can follow the ZPar instruction to complie constituent parser with implementation of "jiangming".
./conparser [in] [out] zpar_model [extra feature]
@article{TACL927,
author = {Liu, Jiangming and Zhang, Yue },
title = {Shift-Reduce Constituent Parsing with Neural Lookahead Features},
journal = {Transactions of the Association for Computational Linguistics},
volume = {5},
year = {2017},
issn = {2307-387X},
url = {https://transacl.org/ojs/index.php/tacl/article/view/927},
pages = {45--58}
}
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