  Despite the long history of named-entity recognition (NER) task in the
natural language processing community, previous work rarely studied the task on
conversational texts. Such texts are challenging because they contain a lot of
word variations which increase the number of out-of-vocabulary (OOV) words. The
high number of OOV words poses a difficulty for word-based neural models.
Meanwhile, there is plenty of evidence to the effectiveness of character-based
neural models in mitigating this OOV problem. We report an empirical evaluation
of neural sequence labeling models with character embedding to tackle NER task
in Indonesian conversational texts. Our experiments show that (1) character
models outperform word embedding-only models by up to 4 $F_1$ points, (2)
character models perform better in OOV cases with an improvement of as high as
15 $F_1$ points, and (3) character models are robust against a very high OOV
rate.
