We have created a dataset of Hindi-English Code-Mixed Social Media Text (tweets) for the task of Named Entity Recognition. Tweets are pre-processed and annotated as per the 6 NER tags and a 7th Other tag.

• B-Per Indicates the Begening of a Person's name.
• I-Per Indicates the intermediate of a Person's name.
• B-Org Indicates the Begening of a Organizations's name.
• I-Org Indicates the intermediate of a Organizations's name.
• B-Loc Indicates the Begening of a Locations's name.
• I-Loc Indicates the intermediate of a Locations's name.
• Other Indicates all the word not falling in any of the above 6.

eg:

• TwitterData folder contains Id's of the scrapped tweets inside Scrapped folder, and processed and annotated data as named inside this.
• All the three Models.py are the files for the three ML classification models we used for our reserach paper.
• preprocessing and vector creation scripts are added with names indicating that.
• This dataset is in development and in future we will extend this to more number of tweets so as to make it a more reliable dataset for this taska and others.

• DecisionTree and CRF models have direct score calls that gives all the required stats.
• Keras does not provide the same for displaying score stats for LSTM model, so we build a coustom call of all the measure values and took average over all the iterations (here 5).
• All the models performed well on the given data.
• Decision Tree model with a f1-score of 0.94.
• Conditional Random Field (CRF) model with a f1-score of 0.95.
• LSTM model with a f1-score of 0.95.

• Vinay Singh
• Deepanshu Vijay
• Syed A. Sarfaraz
• Manish Srivastava

LTRC IIIT-Hyderabad

Named Entity Recognition for Hindi-English Code-Mixed Social Media Text

2018, 27-35, Proceedings of the Seventh Named Entities Workshop here