An Approach for a Next‐Word Prediction for Ukrainian Language

Shakhovska, Khrystyna; Dumyn, Iryna; Kryvinska, Natalia; Kagita, Mohan Krishna

doi:10.1155/2021/5886119

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…Next word prediction is one of the highly studied topics of the NLP domain. LSTM and Markov's chain were implemented for next-word prediction in the Ukrainian language (Shakhovska et al, 2021). Stremmel and Singh (2021) used the LSTM-RNN language model implemented by Jing and Xu (2019) for the next word prediction.…”

Section: Related Workmentioning

confidence: 99%

Dzongkha Next Words Prediction Using Bidirectional LSTM

Wangchuk¹,

Wangchuk

Namgyel

2023

bjrd

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Dzongkha Development Commission of Bhutan (DDC) is trying to computerize Dzongkha. However, the computerization of Dzongkha poses numerous challenges. Currently, the support for Dzongkha in modern technology is limited to printing, typing, and storage. Typewriting a single Dzongkha word requires several keypresses. As a result, typing Dzongkha is tedious. In this paper, the Dzongkha word label prediction was studied. The purpose of the study was to further reduce keystrokes and make Dzongkha typing much faster. The dataset encompasses different genres curated by DDC. The dataset consisted of 10000 sentences and 4820 unique words. Next, 52150 sequences were generated using N-gram methods followed by vectorizing text using embedding techniques. Different RNN-based models were evaluated for the next Dzongkha words prediction. Two Bi-LSTM layers with 512 hidden layer neurons gave the best accuracy of 73.89% with a loss of 1.0722.

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Section: Related Workmentioning

confidence: 99%

Dzongkha Next Words Prediction Using Bidirectional LSTM

Wangchuk¹,

Wangchuk

Namgyel

2023

bjrd

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“…Understanding user behavior, on the other hand, pushes companies to improve the Predicting the next word using the MCM according to profiling personality quality of their various products and services [3], and then present their products to fit the user's desire [6].When searching in search engines or writing on a specific platform, however, we notice that most solutions suggest words or sentences that follow what we have already written as the proposition of what we want to write. Its recommendations are based on past research done by other users and even by their regions [26]. Sometimes the user does not object if he finds suggestions that contradict his principles, culture, or general personality.…”

Section: Problem Formulationmentioning

confidence: 99%

Predicting the next word using the Markov chain model according to profiling personality

2023

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Understanding human data has been the focus of philosophers and scientists. Social media platforms encourage people to be creative and share their personal information. By analyzing data, we will be able to identify people's personalities and information that is also important to specific profils. The aim of this paper is to propose an approach that predicts the next word during writing a sentence based on the user's personality. To achieve this goal, our approach is illustrated by two points:(1) An approximate extraction of the Big Five model for a specific user from his tweets. (2) Predicting the next word while a user is writing a new tweet depends on his personality using the Markov Chain Model. On the basis of these two notions, our approach makes writing posts easier by predicting and suggesting next words based on the user's personality. Experience represents the ease of predicting the next word during the writing of a new post related to individual potential.

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“…In [25], some sequence prediction models were explored to evaluate the performance of the next word prediction for Hindi. The same analogy is applied to Ukrainian language [26] to evaluate next word prediction models performance for existing sequence models.…”

Section: Literature Reviewmentioning

confidence: 99%

Context-based Bengali Next Word Prediction: A Comparative Study of Different Embedding Methods

Mahbub

Akhter

Kabir

et al. 2023

Dhaka Uni. J. of Applied Sci. and Eng.

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Next word prediction is a helpful feature for various typing subsystems. It is also convenient to have suggestions while typing to speed up the writing of digital documents. Therefore, researchers over time have been trying to enhance the capability of such a prediction system. Knowledge regarding the inner meaning of the words along with the contextual understanding of the sequence can be helpful in enhancing the next word prediction capability. Theoretically, these reasonings seem to be very promising. With the advancement of Natural Language Processing (NLP), these reasonings are found to be applicable in real scenarios. NLP techniques like Word embedding and sequential contextual modeling can help us to gain insight into these points. Word embedding can capture various relations among the words and explain their inner knowledge. On the other hand, sequence modeling can capture contextual information. In this paper, we figure out which embedding method works better for Bengali next word prediction. The embeddings we have compared are word2vec skip-gram, word2vec CBOW, fastText skip-gram and fastText CBOW. We have applied them in a deep learning sequential model based on LSTM which was trained on a large corpus of Bengali texts. The results reveal some useful insights about the contextual and sequential information gathering that will help to implement a context-based Bengali next word prediction system. DUJASE Vol. 7 (2) 8-15, 2022 (July)

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An Approach for a Next‐Word Prediction for Ukrainian Language

Cited by 19 publications

References 16 publications

Dzongkha Next Words Prediction Using Bidirectional LSTM

Dzongkha Next Words Prediction Using Bidirectional LSTM

Predicting the next word using the Markov chain model according to profiling personality

Context-based Bengali Next Word Prediction: A Comparative Study of Different Embedding Methods

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