Parts-of-Speech tagging for Malayalam using deep learning techniques

Akhil, K. K.; Rajimol, R.; Anoop, V. S.

doi:10.1007/s41870-020-00491-z

Cited by 20 publications

(8 citation statements)

References 13 publications

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“…When the composition corpus is part-of-speech labeling, the labeling accuracy is 0.953. Akhil et al [27] used a multilayer neural network to divide the labeling process into two steps. In addition, in the last layer, CRF is used for labeling.…”

Section: Results Analysis Of Part-of-speechmentioning

confidence: 99%

“…Stop word filtering technology can help us to remove stop words that are not helpful to the semantic information of the content, to reduce the interference of the text content with real semantic information. Parts-of-speech tagging is the process of determining the grammatical category of each word in a given sentence, determining its part of speech, and adding a tag [27]. Part-of-speech tagging is a very basic work.…”

Section: Automatic Scoring Model For Englishmentioning

confidence: 99%

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Automatic Integrated Scoring Model for English Composition Oriented to Part-Of-Speech Tagging

Chen

2021

Complexity

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Part-of-speech tagging for English composition is the basis for automatic correction of English composition. The performance of the part-of-speech tagging system directly affects the performance of the marking and analysis of the correction system. Therefore, this paper proposes an automatic scoring model for English composition based on article part-of-speech tagging. First, use the convolutional neural network to extract the word information from the character level and use this part of the information in the coarse-grained learning layer. Secondly, the word-level vector is introduced, and the residual network is used to establish an information path to integrate the coarse-grained annotation and word vector information. Then, the model relies on the recurrent neural network to extract the overall information of the sequence data to obtain accurate annotation results. Then, the features of the text content are extracted, and the automatic scoring model of English composition is constructed by means of model fusion. Finally, this paper uses the English composition scoring competition data set on the international data mining competition platform Kaggle to verify the effect of the model.

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Section: Results Analysis Of Part-of-speechmentioning

confidence: 99%

Section: Automatic Scoring Model For Englishmentioning

confidence: 99%

Automatic Integrated Scoring Model for English Composition Oriented to Part-Of-Speech Tagging

Chen

2021

Complexity

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“…In low resourced language, POS tagging is also a crucial step in NLP, so the increase of the number of research recently. In the Malayalam language, Akhil et al [19] proposes a deep learning model for POS tagging. They used the available Malayalam corpus of 280000 tokens and trained different RNN models including GRU, LSTM and Bi-LSTM that shows better results than the available POS taggers reaching 98% in terms of F-measure.…”

Section: Literature Review In Pos Tagging Low Resourced Language Usin...mentioning

confidence: 99%

Deep Neural Networks for Part-of-Speech Tagging in Under-Resourced Amazigh

Bani¹,

Amri²,

Zenkouar³

et al. 2023

RIA

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Part-of-speech (POS) tagging denotes the assignment of appropriate grammatical categories to individual words within a sentence or text, playing a pivotal role in numerous natural language processing (NLP) tasks. While POS tagging in widely-used languages such as English has reached accuracy levels exceeding 97%, less-resourced languages such as Amazigh have seen limited research and therefore, less accuracy in tagging efforts. This paper aims to bridge this gap by exploring the application of deep learning models for Amazigh POS tagging, specifically focusing on various types of recurrent neural networks (RNNs)-gated recurrent networks, long short-term memory (LSTM) networks, and bidirectional LSTM networks. Despite the relatively small dataset of 60k tokens, a stark contrast to the vast corpuses available for languages with extensive resources, the proposed RNN models have demonstrated significant improvements over existing Amazigh POS taggers. Remarkably, all RNN models tested in this study outperformed traditional machine learning taggers, achieving an accuracy rate of 97%, thus presenting a promising avenue for enhanced POS tagging in under-resourced languages. This research underscores the potential of deep learning approaches in contributing to the advancement of linguistic studies in less-documented languages, such as Amazigh.

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“…Looking to resolve ambiguity with a deep learning method in Malayalam [19]. Deep learning approaches improve accuracy and effective performance for various tasks in which the purposes of the neural network itself need to be developed [20]. The main contributions of this paper can be summarized as follows: − Pre-processing stage is used for sentence alignment, followed by a deep morph analyzer module that performs the morphological analysis of Sanskrit and Malayalam sentences.…”

Section:  Issn: 2502-4752mentioning

confidence: 99%

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

Chingamtotattil

Gopikakumar

2022

IJEECS

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Neural machine translation (NMT) is a fast-evolving MT paradigm and showed good results, particularly in large training data circumstances, for several language pairs. In this paper, we have utilized Sanskrit to Malayalam language pair neural machines translation. The attention-based mechanism for the development of the machine translation system was particularly exploited. Word sense disambiguation (WSD) is a phenomenon for disambiguating the text to let the machine infer the proper definition of the particular word. Sequential deep learning approaches such as a recurrent neural network (RNN), a gated recurrent unit (GRU), a long short term memory (LSTM), and a bi-directional LSTM (BLSTM) were used to analyze the tagged data. By adding morphological elements and evolutionary word sense disambiguation, the suggested common character-word embedding-based NMT model gives a BLEU score of 38.58 which was higher than the others.

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Parts-of-Speech tagging for Malayalam using deep learning techniques

Cited by 20 publications

References 13 publications

Automatic Integrated Scoring Model for English Composition Oriented to Part-Of-Speech Tagging

Automatic Integrated Scoring Model for English Composition Oriented to Part-Of-Speech Tagging

Deep Neural Networks for Part-of-Speech Tagging in Under-Resourced Amazigh

Neural machine translation for Sanskrit to Malayalam using morphology and evolutionary word sense disambiguation

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