Modify README.md

whustan · Aug 21, 2019 · fa06c27 · fa06c27
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Showing 1 changed file with 6 additions and 4 deletions.
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@@ -47,19 +47,21 @@ BMES tag scheme, with each character its label for one line. Sentences are split
 		               --saved_model saved_model/model_onto4ner \
 		               --saved_set data/onto4ner.cn/saved.dset
 		               
-## Data Downloads
+## Data
 
 The pretrained character and word embeddings can be downloaded from [Lattice LSTM](https://github.com/jiesutd/LatticeLSTM).
 
-Datasets including OntoNotes, MSRA, Weibo and Resume are available at Google Drive or Baidu Pan.
+Original datasets can be found at [OntoNotes](https://catalog.ldc.upenn.edu/LDC2011T03), [MSRA](http://sighan.cs.uchicago.edu/bakeoff2006/), 
+[Weibo](https://github.com/hltcoe/golden-horse) and [Resume](https://github.com/jiesutd/LatticeLSTM/tree/master/ResumeNER).
+If you want preprocessed datasets that satisfy the input format of our codes, please contact me.
 
 ## Pretrained Model Downloads
 
 We also provide pretrained models on the four datasets, which are the same models as reported in the paper.
 If you try to retrain models from scratch under the same hyper-parameter settings, you may obtain a sightly 
 lower or higher F1 score than that reported in the paper (in our experiments we selected the model that performed best).
 
-Pretrained models and related hyper-parameter settings are available at Google Drive or Baidu Pan.
+Pretrained models and related hyper-parameter settings are available at Google Drive or Baidu Pan (TBC).
 
 When running main.py in test mode for pretrained models, you can get the results as follows:
 
@@ -74,7 +76,7 @@ When running main.py in test mode for pretrained models, you can get the results
 | Resume dev     |   94.27   |  94.59  | 94.43 |
 | Resume test    |   95.28   |  95.46  | 95.37 |
 
-## Cite
+## Citation
 
 	@article{gui2019lexicon,  
 	 title={A Lexicon-Based Graph Neural Network for Chinese NER},