Text preprocessing is the bottleneck of text classification.
文本预处理是文本分类的瓶颈。
The text preprocessing, feature selection, training algorithm, and recognition method are described in the paper.
对文本分类系统的系统结构、预处理、特征提取、训练算法、分类算法等进行了详细介绍。
Text classification is build up by the text preprocessing, feature thesaurus creation, the text classifier, and testing of text classification results.
文本分类由文本预处理，特征词库的建立、文本分类器、文本分类结果测试这几个部分组成。
In this paper, techniques for image text analysis are divided into three main blocks: image text extraction, image text preprocessing and image text recognition.
我们将图像文本分析技术划分为三大组成部分：图像文本定位、图像文本的预处理和图像文本的识别进行讨论。
This technology is an integrated application of many natural language processing techniques, including text preprocessing, text structure analysis, inter-text inference and so on.
该技术是许多自然语言处理技术的综合运用，涉及的内容包括文本预处理、文本结构分析、篇章关联推导等。
Therefore, it's useful for editing command output or for when you're preprocessing a file with other tools — you can then pipe that text output straight to sed for quick editing.
因此，它对于编辑命令输出或对于使用其他工具对文件进行预处理非常有用——然后您可以将该文本通过管道直接输出给sed，以进行快速编辑。
The text chunking, as a preprocessing step for parsing, is to divide text into syntactically related non-overlapping groups of words (chunks), reducing the complexity of the full parsing.
文本组块分析作为句法分析的预处理阶段，通过将文本划分成一组互不重叠的片断，来达到降低句法分析的难度。
A preprocessing step performs macro substitution on program text, inclusion of other source files, and conditional compilation.
编译的预处理阶段对程序文本进行宏替换，包含进其他源文件，并进行条件编译。
The text chunking, as a preprocessing step for parsing, is to divide text into syntactically related non-overlapping groups of words (chunks), reducing the complexity of the ful.
文本组块分析作为句法分析的预处理阶段，通过将文本划分成一组互不重叠的片断，来达到降低句法分析的难度。
Chapter 3 mainly researches the method of converting semi-structured emails to structured text data in mail preprocessing, especially the method of recognizing the potential characters of emails, etc.
第三章重点研究了在邮件预处理方面将半结构化的电子邮件转化为结构化的文本数据方法，特别是电子邮件潜在特征词的识别方法等。
Feature selection is frequently used as a preprocessing step to text classification, which is effective in reducing dimensionality and increasing classification accuracy.
特征选择是文档分类中常见的预处理工作，通过对文档特征空间降维，可以提高文档的分类性能。
Feature selection is frequently used as a preprocessing step to text classification, which is effective in reducing dimensionality and increasing classification accuracy.
特征选择是文档分类中常见的预处理工作，通过对文档特征空间降维，可以提高文档的分类性能。
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