This implementation is based on the dynet 1.0 library for this software to function. The paper is "Attention Modeling for Targeted Sentiment".

Building

mkdir build
cd build
cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen
make    

Training and Testing

The training command is

./attention_context_gated -T [training data] -d [development data] --test_data [test data] --pretrained [pretrained word embeddings] --lexicon sentiment140.lex --report_i 500 --dev_report_i 10 --dynet-mem 1024 --training_methods 1

The test command is

./attention_context_gated -T [training data] -d [development data] --test_data [test data] --pretrained [pretrained word embeddings] --lexicon sentiment140.lex --report_i 500 --dev_report_i 10 --dynet-mem 1024 --train_methods 1 --count_limit 1000 --test --model [trained model]

Noted that the sentiment140.lex is imported, but not used, in order to ensure the consistent of the word indexes in the trained model. The trained model is provided in model/model.bz2. You need to uncompress it first, and use it as the trained model.

Citation

@InProceedings{liu-zhang:2017:EACLshort,
     author    = {Liu, Jiangming  and  Zhang, Yue},
     title     = {Attention Modeling for Targeted Sentiment},
     booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
     month     = {April},
     year      = {2017},
     address   = {Valencia, Spain},
     publisher = {Association for Computational Linguistics},
     pages     = {572--577},
     url       = {http://www.aclweb.org/anthology/E17-2091}
     }