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首页> 外文期刊>International journal of knowledge and systems science >A New Approach Using Hidden Markov Model and Bayesian Method for Estimate of Word Types in Text Mining
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A New Approach Using Hidden Markov Model and Bayesian Method for Estimate of Word Types in Text Mining

机译：基于隐马尔可夫模型和贝叶斯方法的文本挖掘词类型估计新方法

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摘要

Determining the structure of words in the text for the operations such as automated information extraction and text summarization of the text is essential. In computers, textual analysis to define the type of the word is considered as a vital advantage. Defining the types of words provides an estimate of the sequence of words in the sentence. In this article, estimating types of Turkish words is provided by developing a Hidden Markov Model and a Bayesian-based new model. In this model, an algorithm is developed which separates the suffixes of the words and grouping the words by counts of characters that suffixes of the words receive. A text composed of 584 Turkish words is used for the testing the dependability of the model. The model has achieved a high success rate in predicting the types of Turkish words.
机译：确定诸如自动信息提取和文本的文本摘要之类的操作中的文本中的单词结构至关重要。在计算机中，定义单词类型的文本分析被认为是至关重要的。定义单词的类型可以估算句子中单词的顺序。在本文中，通过开发“隐马尔可夫模型”和基于贝叶斯的新模型来估计土耳其语单词的类型。在此模型中，开发了一种算法，该算法将单词的后缀分开，并按单词后缀接收的字符计数对单词进行分组。由584个土耳其语单词组成的文本用于测试模型的可靠性。该模型在预测土耳其语类型方面取得了很高的成功率。

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