Thai Named Entity Corpus Annotation Scheme and Self Verification by BiLSTM-CNN-CRF

Corpus is one of the essential parts of language research, especially for the low resource language. To ensure the researching result to be most effective, the corpus that has been used also requires effectiveness and accuracy. The Thai language has some special characteristics that cause difficulty in building the corpus and affect the error of those corpora. Therefore, this paper proposes an effective and efficient approach to clean up the existing Named Entity corpus before using it in any language research. The THAI-NEST corpus is adopted to verify the consistency and integrity of the data and re-design with our proposed model. The revised corpus is verified by the BiLSTM-CNN-CRF model that combined the features among word, POS, and Thai character clusters (TCCs). Experimental results show the effectiveness of the verification, which increased the accuracy by up to 12%, and the model can effectively detect and handle errors of word segmentation and NE tag consistency.