Sentence Boundary Detection in Kannada Language

N, Deepamala; P, Ramakanth Kumar.

doi:10.5120/4852-7124

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…We use a deep learning approach to detect the sentence boundary as in our previous work [21]. 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].…”

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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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Section: Introductionmentioning

confidence: 99%

Indonesian Sentence Boundary Detection using Deep Learning Approaches

et al. 2021

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Text Classification of Kannada Webpages Using Various Pre-processing Agents

N¹,

Kumar²

2014

Advances in Intelligent Systems and Computing

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Abstract. Text classification of Webpages has wide applications and many techniques have been employed to achieve the same. In this paper, an attempt is made to classify Kannada webpages into pre-determined 6 classes or categories. Kannada is a morphologically rich Indian Language. Kannada Webpages are subjected to different pre-processing steps and machine learning techniques like Naïve Bayes and Maximum Entropy are applied to train models. All the pre-processing steps before classification are implemented as intelligent agents doing a particular task like Language Identification, Sentence Boundary detection and Term frequency calculation. It is observed that highest accuracy of 0.9 is achieved using both stemming and stopword removal.

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Sentence Tokenization Using Statistical Unsupervised Machine Learning and Rule-Based Approach for Running Text in Gujarati Language

Tailor

Patel

2018

Advances in Intelligent Systems and Computing

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Sentence Boundary Detection in Kannada Language

Cited by 4 publications

References 7 publications

Indonesian Sentence Boundary Detection using Deep Learning Approaches

Indonesian Sentence Boundary Detection using Deep Learning Approaches

Text Classification of Kannada Webpages Using Various Pre-processing Agents

Sentence Tokenization Using Statistical Unsupervised Machine Learning and Rule-Based Approach for Running Text in Gujarati Language

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