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首页> 外文期刊>International Journal of Intelligent Systems and Applications >An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction
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An Exploratory Approach to Find a Novel Metric Based Optimum Language Model for Automatic Bangla Word Prediction

机译：一种新的基于度量的最佳孟加拉语言自动预测语言模型的探索方法

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Word completion and word prediction are two important phenomena in typing that have intense effect on aiding disable people and students while using keyboard or other similar devices. Such auto completion technique also helps students significantly during learning process through constructing proper keywords during web searching. A lot of works are conducted for English language, but for Bangla, it is still very inadequate as well as the metrics used for performance computation is not rigorous yet. Bangla is one of the mostly spoken languages (3.05% of world population) and ranked as seventh among all the languages in the world. In this paper, word prediction on Bangla sentence by using stochastic, i.e. N-gram based language models are proposed for auto completing a sentence by predicting a set of words rather than a single word, which was done in previous work. A novel approach is proposed in order to find the optimum language model based on performance metric. In addition, for finding out better performance, a large Bangla corpus of different word types is used.
机译：单词补全和单词预测是打字过程中的两个重要现象，它们在使用键盘或其他类似设备帮助残疾人和学生时会产生很大的影响。这种自动完成技术还可以通过在网络搜索过程中构造适当的关键字，在学习过程中极大地帮助学生。许多工作都是针对英语进行的，但是对于孟加拉语来说，这仍然是远远不够的，并且用于性能计算的指标还不严格。孟加拉语是最常用的语言之一（占世界人口的3.05％），在世界所有语言中排名第七。在本文中，通过使用随机（即基于N元语法的语言模型）提出了对Bangla句子的单词预测，以通过预测一组单词而不是单个单词来自动完成句子，这是先前工作中所做的。为了找到基于性能指标的最佳语言模型，提出了一种新颖的方法。另外，为了发现更好的性能，使用了不同单词类型的大型Bangla语料库。

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