Texting stands out as the most prominent form of communication worldwide.
Individual spend significant amount of time writing whole texts to send emails
or write something on social media, which is time consuming in this modern era.
Word prediction and sentence completion will be suitable and appropriate in the
Bangla language to make textual information easier and more convenient. This
paper expands the scope of Bangla language processing by introducing a Bi-LSTM
model that effectively handles Bangla next-word prediction and Bangla sentence
generation, demonstrating its versatility and potential impact. We proposed a
new Bi-LSTM model to predict a following word and complete a sentence. We
constructed a corpus dataset from various news portals, including bdnews24, BBC
News Bangla, and Prothom Alo. The proposed approach achieved superior results
in word prediction, reaching 99\% accuracy for both 4-gram and 5-gram word
predictions. Moreover, it demonstrated significant improvement over existing
methods, achieving 35\%, 75\%, and 95\% accuracy for uni-gram, bi-gram, and
tri-gram word prediction, respectively

本研究论文介绍了一种有效处理孟加拉语下一个词预测和孟加拉语句子生成的双向长短期记忆网络模型，扩展了孟加拉语处理的范围，具有多样性和潜在影响力，在各种新闻门户网站上构建了语料库数据集，并在单词预测方面取得了卓越结果，uni-gram、bi-gram 和 tri-gram 的单词预测准确率分别达到 35%、75% 和 95%。

通过扩展的 RNN 与 Bi-LSTM 模型在 N-gram 语言上增强孟加拉语下一个单词预测和句子完成

Enhancing Bangla Language Next Word Prediction and Sentence Completion  through Extended RNN with Bi-LSTM Model On N-gram Language

User-generated content published on microblogging social networks constitutes
a priceless source of information. However, microtexts usually deviate from the
standard lexical and grammatical rules of the language, thus making its
processing by traditional intelligent systems very difficult. As an answer,
microtext normalization consists in transforming those non-standard microtexts
into standard well-written texts as a preprocessing step, allowing traditional
approaches to continue with their usual processing. Given the importance of
phonetic phenomena in non-standard text formation, an essential element of the
knowledge base of a normalizer would be the phonetic rules that encode these
phenomena, which can be found in the so-called phonetic algorithms.
In this work we experiment with a wide range of phonetic algorithms for the
English language. The aim of this study is to determine the best phonetic
algorithms within the context of candidate generation for microtext
normalization. In other words, we intend to find those algorithms that taking
as input non-standard terms to be normalized allow us to obtain as output the
smallest possible sets of normalization candidates which still contain the
corresponding target standard words. As it will be stated, the choice of the
phonetic algorithm will depend heavily on the capabilities of the candidate
selection mechanism which we usually find at the end of a microtext
normalization pipeline. The faster it can make the right choices among big
enough sets of candidates, the more we can sacrifice on the precision of the
phonetic algorithms in favour of coverage in order to increase the overall
performance of the normalization system.
KEYWORDS: microtext normalization; phonetic algorithm; fuzzy matching;
Twitter; texting

通过实验一系列语音算法，研究在候选生成过程中对微文本规范化的最佳语音算法，以提高规范化系统的整体性能。