2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) 2016
DOI: 10.1109/iciev.2016.7760098
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Deep learning based parts of speech tagger for Bengali

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Cited by 27 publications
(2 citation statements)
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“…However, using deep learning methodologies for POS tagging in languages with limited resources poses certain difficulties, primarily due to the limited availability of annotated datasets and the lack of experts during the development of these datasets. Despite these limitations, some studies have applied deep learning to POS tagging in languages such as Malayalam [33], Nepali [34], Bengali [35], Khasi [36], and Korean [37]. Further research in this area is ongoing.…”
Section: Literature Reviewmentioning
confidence: 99%
“…However, using deep learning methodologies for POS tagging in languages with limited resources poses certain difficulties, primarily due to the limited availability of annotated datasets and the lack of experts during the development of these datasets. Despite these limitations, some studies have applied deep learning to POS tagging in languages such as Malayalam [33], Nepali [34], Bengali [35], Khasi [36], and Korean [37]. Further research in this area is ongoing.…”
Section: Literature Reviewmentioning
confidence: 99%
“…It helps us to decide whether to proceed with the upcoming search for further information [6]. For different kinds of tasks such as text classification [1], named entity recognition [2], parts of speech [3][4], sentiment analysis [5], and many other aspects of the text used Deep learning techniques. There is a subtle difference between keyword and keyphrase extraction.…”
mentioning
confidence: 99%