Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

Endalie, Demeke; Haile, Getamesay; Taye, Wondmagegn

doi:10.1371/journal.pone.0273156

Cited by 8 publications

(2 citation statements)

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“…Memory (LSTM) and Bi-LSTM explored for the task of predicting the next word, and an accuracy of 59.46% and 81.07% was observed for LSTM and Bi-LSTM respectively. Endalie et al [13] present a Bi-directional Long Short Term-Gated Recurrent Unit (BLST-GRU) network model for the prediction of the next word for the Amharic Language. They evaluated the proposed network model with 63,300 Amharic sentences, producing 78.6% accuracy.…”

Section: Sharma N Goel Used Two Deep Learning Techniques Namely Long ...mentioning

confidence: 99%

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

Hoque,

Jahan,

Paul

et al. 2023

JDAIP

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We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

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Section: Sharma N Goel Used Two Deep Learning Techniques Namely Long ...mentioning

confidence: 99%

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

Hoque,

Jahan,

Paul

et al. 2023

JDAIP

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“…Because LSTMs may store information from past sequence inputs in the current input state, they have proven a natural option for data applications such as speech recognition, language modeling, and trial option (Niu and Srivastava, 2022). An LSTM has a hidden layer, an input layer, and an output layer (Endalie et al, 2022). The hidden state in a forward LSTM network only saves information from the past.…”

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confidence: 99%

Wastewater pipe defect rating model for pipe maintenance using natural language processing

Betgeri,

Vadyala,

Matthews

et al. 2023

Front. Water

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IntroductionClosed-circuit video (CCTV) inspection has been the most popular technique for visually evaluating the interior status of pipelines in recent decades. Certified inspectors prepare the pipe repair document based on the CCTV inspection. The traditional manual method of assessing structural wastewater conditions from pipe repair documents takes a long time and is prone to human mistakes. The automatic identification of necessary texts has received little attention. Computer Vision based Machine Learning models failed to estimate structural damage because they are not entirely understood and have difficulty providing high data needs. Hence, they have problems providing physically consistent findings due to their high data needs. Currently, a very small curated annotated image and video data set with well-defined, precisely labeled categories to test Computer Vision based Machine Learning models.MethodsThis study provides a valuable method to determine the pipe defect rating of the pipe repair documents by developing an automated framework using Natural Language Processing (NLP) on very small, curated annotated images, video data, and more text data. The text used in this study is broken into grammatical units using NLP technologies. The next step in the analysis entails using words to find the frequency of pipe defects and then classify them into respective defect ratings for pipe maintenance.Results and discussionsThe proposed model achieved 95.0% accuracy, 94.9% recall, 95% specificity, 95.9% precision score, and 95.7% F1 score, showing the potential of the proposed model to be used in large-scale pipe repair documents for accurate and efficient pipeline failure detection to improve the quality of the pipeline.

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Current and Evolving Applications to Speech Processing

2023

AI Applications to Communications and Information Technologies

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Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

Cited by 8 publications

References 18 publications

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

Wastewater pipe defect rating model for pipe maintenance using natural language processing

Current and Evolving Applications to Speech Processing

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