Abstract:
The next word prediction is useful for the users and helps them to write more accurately and
quickly. Next word prediction is vital for the Amharic Language since different characters
can be written by pressing the same consonants along with different vowels, combinations
of vowels, and special keys. As a result, we 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. We evaluate the proposed network model with 63,300 Amharic sen tence and produces 78.6% accuracy. In addition, we have compared the proposed model
with state-of-the-art models such as LSTM, GRU, and BLSTM. The experimental result
shows, that the proposed network model produces a promising result.
