Syllable-based Neural Named Entity Recognition for Myanmar Language

TL;DR

This paper introduces the first neural network-based approach for Myanmar language NER, utilizing syllable-level data and a newly constructed annotated corpus, demonstrating promising results over traditional models.

Contribution

It presents the first evaluation of neural network models for Myanmar NER and develops the first manually annotated Myanmar NER corpus.

Findings

Bidirectional LSTM with CRF achieves highest F-score

Neural models outperform baseline CRF

Constructed the first Myanmar NER corpus

Abstract

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.