BoydCut: Bidirectional LSTM-CNN Model for Thai Sentence Segmenter

Sirirattanajakarin, Sorratat; Jitkongchuen, Duangjai; Intarapaiboon, Peerasak

doi:10.1109/ibdap50342.2020.9245454

Cited by 4 publications

(1 citation statement)

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“…Sentence Boundary Detection has been studied on other languages like English [22], Portuguese [23], French [24], Vietnamese [25], Chinese [26], Japanese [27], Marathi [28], Kannada [29], Arabic [30], and Urdu [31]. Another study is in Thai with Bi-LSTM CNN Approach [32]. In Indonesian, Sentence Boundary Detection has been presented using Maximum Entropy [33] and Bidirectional LSTM [21].…”

Section: Introductionmentioning

confidence: 99%

Indonesian Sentence Boundary Detection using Deep Learning Approaches

et al. 2021

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Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes..

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