(Dataset untuk Membangun Named Entity Recognizer (NER) untuk Bahasa Indonesia)

This repository contains resources of a project named Modified DBpedia Entities Expansion (MDEE) (Alfina, et al., 2017).
We share:

• Three NER datasets used in the experiments explained in the paper (in the main folder), each consists of 20,000 sentences, along with the gold standard.
• Three NER datasets, as the revised version of the three NER datasets in the main folder (in the revised-20k folder).
• The original names in Indonesian DBpedia (in "original-dbpedia" folder).
• Two versions of DBpedia explained in the paper (in "expanded-dbpedia" folder): MDEE, and MDEE_Gazetteer
• A dataset of 48,957 sentences named SINGGALANG (in "singgalang" folder). We used expanded DBpedia of MDEE_Gazetteer to label this dataset.

The datasets conforms with the dataset format of Stanford-NER.

Four named entity classes are used:

• "Person" for person names
• "Place" for place names
• "Organisation" for organization names
• "O" for others

List of dataset in main folder:

1. dataset created using original DEE (Alfina et al., 2016), file name: 20k-dee.txt, with properties file: 20k-dee.prop
2. dataset created using Modified DEE (Alfina et al., 2017), file name: 20k-mdee.txt, with properties file: 20k-mdee.prop
3. dataset created using Modified DEE plus gazetteer (Alfina et al., 2017), file name: 20k-mdee-gazz.txt, with properties file: 20k-mdee-gazz.prop
4. A gold standard created by Luthfi, et al (2014)

We provide a new NER dataset in this repository, named SINGGALANG. The specifications of this dataset are:

• The number of sentences: 48,957
• Generated using expanded DBpedia of MDEE_Gazett (the best version of those three expanded DBpedia)

The dataset may be used for free, but if you want to publish paper/publication using the dataset, please cite these publications:

• The DEE NER dataset: Ika Alfina, Ruli Manurung, and Mohamad Ivan Fanany, "DBpedia Entities Expansion in Automatically Building Dataset for Indonesian NER", in Proceeding of 8th International Conference on Advanced Computer Science and Information Systems 2016 (ICACSIS 2016).

• The MDEE and Singgalang NER dataset: Ika Alfina, Septiviana Savitri, and Mohamad Ivan Fanany, "Modified DBpedia Entities Expansion for Tagging Automatically NER Dataset", in Proceeding of 9th International Conference on Advanced Computer Science and Information Systems 2017 (ICACSIS 2017).

• The Gold Standard: Andry Luthfi, Bayu Distiawan, and Ruli Manurung, "Building an Indonesian named entity recognizer using Wikipedia and DBPedia", in the Proceesing of 2014 International Conference on Asian Language Processing (IALP 2014).

We suggest you to use the Stanford NER library.
The steps to create NER model using Stanford NER library are as follows:

1. Download Stanford-NER.

2. Download the dataset and its properties file (file with .prop extension)

3. Use Stanford NER classifier to create the model.
   For example:
   java -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -prop 20k-mdee.prop
   

   I recommend to increase the heap size so you can train the dataset on computer with limited RAM. Add option like "-Xmx1024m" on the command, for example:
   

   java -Xmx1024m -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -prop 20k-mdee.prop
   

   if this still doesn't work, increase the number. For example: "-Xmx8000m". This works for me :)

   Let say this step will create a NER model file named "idner-model-20k-mdee.ser.gz"

4. Create or use a testing dataset. Lets say the file name is "testing.txt"

5. Evaluate the NER model using Stanford NER library
   For example:
   java -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier idner-model-20k-mdee.ser.gz -testFile testing.txt