TY - JOUR
T1 - An Approach for a Next-Word Prediction for Ukrainian Language
AU - Shakhovska, Khrystyna
AU - Dumyn, Iryna
AU - Kryvinska, Natalia
AU - Kagita, Mohan Krishna
N1 - Publisher Copyright:
© 2021 Khrystyna Shakhovska et al.
PY - 2021/8/15
Y1 - 2021/8/15
N2 - Text generation, in particular, next-word prediction, is convenient for users because it helps to type without errors and faster. Therefore, a personalized text prediction system is a vital analysis topic for all languages, primarily for Ukrainian, because of limited support for the Ukrainian language tools. LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model presents adequate results but it works slowly. Using the model, user can generate not only one word but also a few or a sentence or several sentences, unlike T9.
AB - Text generation, in particular, next-word prediction, is convenient for users because it helps to type without errors and faster. Therefore, a personalized text prediction system is a vital analysis topic for all languages, primarily for Ukrainian, because of limited support for the Ukrainian language tools. LSTM and Markov chains and their hybrid were chosen for next-word prediction. Their sequential nature (current output depends on previous) helps to successfully cope with the next-word prediction task. The Markov chains presented the fastest and adequate results. The hybrid model presents adequate results but it works slowly. Using the model, user can generate not only one word but also a few or a sentence or several sentences, unlike T9.
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U2 - 10.1155/2021/5886119
DO - 10.1155/2021/5886119
M3 - Article
AN - SCOPUS:85113802966
VL - 2021
JO - Wireless Communications and Mobile Computing
JF - Wireless Communications and Mobile Computing
SN - 1530-8669
M1 - 5886119
ER -