  Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar
natural language processing research work. In this work, NER for Myanmar
language is treated as a sequence tagging problem and the effectiveness of deep
neural networks on NER for Myanmar language has been investigated. Experiments
are performed by applying deep neural network architectures on syllable level
Myanmar contexts. Very first manually annotated NER corpus for Myanmar language
is also constructed and proposed. In developing our in-house NER corpus,
sentences from online news website and also sentences supported from
ALT-Parallel-Corpus are also used. This ALT corpus is one part of the Asian
Language Treebank (ALT) project under ASEAN IVO. This paper contributes the
first evaluation of neural network models on NER task for Myanmar language. The
experimental results show that those neural sequence models can produce
promising results compared to the baseline CRF model. Among those neural
architectures, bidirectional LSTM network added CRF layer above gives the
highest F-score value. This work also aims to discover the effectiveness of
neural network approaches to Myanmar textual processing as well as to promote
further researches on this understudied language.
