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@@ -1,6 +1,9 @@
 ## Better Modeling of Incomplete Annotation for Named Entity Recognition 
 
 This repository implements an LSTM-CRF model for named entity recognition. The model is same as the one by [Lample et al., (2016)](http://www.anthology.aclweb.org/N/N16/N16-1030.pdf) except we do not have the last `tanh` layer after the BiLSTM.
+The code provided is used for the paper "[Better Modeling of Incomplete Annotation for Named Entity Recognition](http://www.statnlp.org/research/ie/zhanming19naacl-ner.pdf)" published in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (*NAACL*).
+
+__NOTE: I'm planning to implement a (more user-friendly) pytorch version of this project. Let me know in the issue if you are interested in that.__
 
 ### Requirements
 * DyNet 2.0 
@@ -25,3 +28,19 @@ python3 our_approach.py --embedding_file ${PATH_TO_EMBEDDING} --model_type our_h
 ```
 
 Change the `model_type` to `our_soft` to run our soft variant. 
+
+
+### Future Work
+Working on a Neural Partial CRF Suite with PyTorch, which should be a neural network version of the [partial-CRF suite](https://github.com/Oneplus/partial-crfsuite).
+
+### Citation
+If you use this software for research, please cite our paper as follows:
+
+```
+@inproceedings{jie2019better,
+  title={Better Modeling of Incomplete Annotations for Named Entity Recognition},
+  author={Jie, Zhanming and Xie, Pengjun and Lu, Wei and Ding, Ruixue and Li, Linlin},
+  booktitle={Proceedings of NAACL},
+  year={2019}
+}
+```
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