Others rely on automatic text classification techniques.
其他方式依赖于自动文本分类技术。
Various text classification methodologies may yield different types of "models," or statistical descriptions of the world.
各种文本分类方法可以生成不同类型的 “模型”，即对真实环境的统计学描述。
For example, NLTK has a whole framework for text classification using statistical techniques like "naive Bayesian" and "maximum entropy" models.
例如，NLTK有一个完整的框架，用于通过类似于“naiveBayesian”和“maximumentropy”等模型的统计技术进行文本分类。
Therefore word segmentation is a key sub-problem of Chinese information processing, such as machine translation, information retrieval and text classification.
因此，分词在机器翻译、信息检索、文本分类等中文信息处理的各项任务中都发挥着基础性的重要作用。
Automatic text classification is defined as the task to assign pre-defined category labels to documents.
文本自动分类是在给定的分类体系下，让计算机根据文本的内容确定与它相关联的类别。
SVM for text classification - tutorial on machine learning?
SVM文本分类的机器学习教程？
Text preprocessing is the bottleneck of text classification.
文本预处理是文本分类的瓶颈。
SVM for text classification - tutorial on machine learning? How do I get started?
SVM文本分类的机器学习教程？我如何开始？
This paper presents a fast text classification algorithm based on KNN (K Nearest Neighbor).
提出了一种基于K近邻(KNN)原理的快速文本分类算法。
This article aims to improve the algorithm of term weighting in automated text classification.
文章研究并改进了文本自动分类中的特征权重算法。
This paper presents an automated text classification model based on projection pursuit regression.
提出了一种基于投影寻踪回归的文本自动分类模型。
Based on DragPush strategy, the paper introduces a text classification incremental learning model, named ICCDP.
基于拉推策略的基本思想，该文提出了文本分类的增量学习模型ICCDP。
It plays an instructional role in academic study and practical application of KNN text classification algorithm.
对KNN文本分类算法的理论研究和实际应用起了指导作用。
At present, the problem of overcoming labeling bottlenecks is the hotspot research filed of text classification.
当前，克服标记瓶颈问题是文本分类中的热点研究领域。
In text classification, we advance an automatic text classification way based on clustering and Rough Set Theory.
在文本分类方面，本文提出了一种基于聚类和粗糙集理论相结合的文本自动分类方法。
The definition of proximate rule was proposed based on the characteristic of text classification rule extraction.
结合文本分类规则抽取的特点，给出了近似规则的定义。
This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
This article studies the statistical automatic text classification methods in the National Science Digital Library.
本文在国家科学数字图书馆的应用背景下，研究基于统计的自动文本分类方法。
The experiment result represents that it can improve the precision and performance of text classification efficiently.
结果表明，该方法可以有效提高文本分类的精度和效能。
Effective dimensionality reduction could make the learning task more efficient and more accurate in text classification.
在文本分类中，有效的维数约简可以提高学习任务的效率和分类性能。
Researches on automatic text classification in Kazakh and develops a system of automatic text classification for Kazakh.
研究哈萨克语自动文本分类，并实现哈萨克语文本自动分类系统。
Two important research directions of text classification are: feature selection method and text classification algorithm.
文本分类的两个重要的研究方向是：特征选择与文本分类算法。
This paper put forward and carried out a text classification method using feed-forward neural network and K-nearest neighbor.
提出并实现了一种结合前馈型神经网络和K最近邻的文本分类算法。
The research can be applied in information retrieval, information filtering, text classification, webpage classification, etc.
该研究可应用于信息检索、信息过滤、文本自动分类、网页自动分类等应用领域。
According to the common characteristics presented by a large number of words, a context-based text classification method is put forward.
针对文本数据中存在大量词语共现的特点，提出了一种基于上下文的文本分类方法。
Based on web mining technology, automatic text classification has become a hot research area in the field of data mining and net mining.
文本数据分类以文本挖掘技术为基础与核心，是近年来数据挖掘和网络挖掘领域当中的一个研究热点。
Therefore it adjusts to human's mode of thinking and further improves the efficiency, correctness and reliability of text classification.
更符合人类的思维方式，从而进一步提高了文本分类的效率、准确性和可靠性。
In addition, a text classification system based on Vector Space Model is studied and a new method for calculating word weight is proposed.
此外，本文还研究了基于向量空间模型的自动文本分类方法，提出了一个新的词权重计算方法，该方法有效提高了分类精度。
The rapid growth in the amount of electronic documents brings both great opportunities and real challenges for automatic text classification.
电子文档的飞速增长为自动文本分类提供了巨大的机遇和挑战。
The rapid growth in the amount of electronic documents brings both great opportunities and real challenges for automatic text classification.
电子文档的飞速增长为自动文本分类提供了巨大的机遇和挑战。
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