adding PT-br NER dataset twitter

Portuguese/Peres2017/readme.txt

@@ -0,0 +1,28 @@
+To comply with Twitter's Terms of Service, only tweet IDs are provided. To download the corresponding full tweets using your own Twitter API credentials.
+The first line ("tweet_id start_pos end_pos named_entity_type") of the file is added to specify the above information regarding the annotations, so it is not part of the actual annotations.
+
+If you use the dataset, please refer to the following paper:
+
+@inproceedings{Peres:2017:BLC:3148011.3154478,
+ author = {Peres, Rafael and Esteves, Diego and Maheshwari, Gaurav},
+ title = {Bidirectional LSTM with a Context Input Window for Named Entity Recognition in Tweets},
+ booktitle = {Proceedings of the Knowledge Capture Conference},
+ series = {K-CAP 2017},
+ year = {2017},
+ isbn = {978-1-4503-5553-7},
+ location = {Austin, TX, USA},
+ pages = {42:1--42:4},
+ articleno = {42},
+ numpages = {4},
+ url = {http://doi.acm.org/10.1145/3148011.3154478},
+ doi = {10.1145/3148011.3154478},
+ acmid = {3154478},
+ publisher = {ACM},
+ address = {New York, NY, USA},
+ keywords = {Deep learning, informal text, machine learning, named entity recognition, natural language processing, neural networks},
+} 
+
+Paper: https://dl.acm.org/citation.cfm?doid=3148011.3154478
+
+
+

Portuguese/README.MD

@@ -64,4 +64,14 @@ A dataset for named entity recognition in Brazilian legal documents is, unlike o
 - **cite**: ["LeNER-Br: a Dataset for Named Entity Recognition in Brazilian Legal Text"](leNER-Br/luz_etal_propor2018.pdf)
 - **web**: https://cic.unb.br/~teodecampos/LeNER-Br/
 
+Peres 2017
+========
+A dataset for named entity recognition in Brazilian Portuguese (#noisydata #twitter)
+
+- **data**:
+  - [dataset.ptbr.twitter.train.ner](Peres2017/dataset.ptbr.twitter.train.ner)
+  - [dataset.ptbr.twitter.test.ner](Peres2017/dataset.ptbr.twitter.test.ner)  
+
+- **cite**: ["Bidirectional LSTM with a Context Input Window for Named Entity Recognition in Tweets"](Peres2017peres2017.pdf)
+
 

README.md

@@ -7,6 +7,7 @@ Datasets to train supervised classifiers for Named-Entity Recognition
  * WikiNER
  * Paramopama
  * leNER-br
+ * Peres2017
 
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 ### German