Abstract
The challenge of the Named Entity Recognition on the domain Thai Poem Klon-Suphap comprise of incomplete sentences, prosody, word transformation and art in language. In this article, we propose the Name Entity Recognition on the domain Thai Poem Klon-Suphap by using The Bidirectional Long Short Term network (BiLSTM) 2 models (1) BiLSTM with words embedding and (2) BiLSTM with words embedding and part of speech embedding. There were 6,216 sentences (waks) of Thai poem Phra-Aphai-Mani. The training data 4,972 sentences and testing data 1,244 sentences to recognize (1) Activity (2) Person (3) Location (4) Number (5) Body (6) Time (7) Animal and (8) Others. The experimental results of BiLSTM with words embedding and part of speech embedding showed the Precision equal 0.89, the Recall equal 0.80 and the F-measure equal 0.84. The accuracy of results is higher than BiLSTM with words embedding.
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Khongtum, O., Promrit, N., Waijanya, S. (2019). The Entity Recognition of Thai Poem Compose by Sunthorn Phu by Using the Bidirectional Long Short Term Memory Technique. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_9
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