Named entity recognition on Indonesian Twitter posts using long short-term memory networks

The task of Named-Entity Recognition (NER) can support the higher-level tasks such as question answering, text summarization, and information retrieval. This work views NER on Indonesian Twitter posts as a sequence labeling problem using supervised machine learning approach. The architecture used is Long Short-Term Memory Networks (LSTMs), with word embedding and POS tag as the model features. As the result, our model can give a performance with an F1 score of 77.08%.