Thai Named Entity Recognition Using BiLSTM-CNN-CRF Enhanced by TCC

Abstract

The languages spoken in Asia share common morphological analysis errors in word segmentation which normally propagate to higher-level processing, i.e., part-of-speech (POS) tagging, syntactic parsing, word extraction, and named entity recognition (NER), as we discuss in this research. We introduce the Thai character cluster (TCC) to reduce the errors propagated from word segmentation and POS tagging by incorporating it into the character representation layer of bidirectional long short-term memory (BiLSTM) for NER. The initial NER model is created from the original THAI-NEST named-entity (NE) tagged corpus by applying the best performing BiLSTM-CNN-CRF model (the combination of BiLSTM, convolutional neural network (CNN), and conditional random field (CRF)) with the word, POS, and TCC embedding. We determine the errors and improve the consistency of the NE annotation through our holdout method by retraining the model with the corrected training set. After the iteration, the overall result of the annotation F1-score has been improved to reach 89.22%, which improves 16.21% from the model trained on the original corpus. The result of our iterative verification is a promising method for low resource language modeling. As a result, The NE silver standard corpus is newly generated for the Thai NER task, called Bangkok Data NE tagged Corpus (BKD). The consistency of annotation is checked and revised according to the improvement of the scope of NE detection by TCC which can recover the errors in word segmentation.

Keywords

• Low-resource languages
• named entity recognition
• Thai language
• Thai named entity annotation corpus