邮件可以包含短文本字符串、查询输出或附件。
A mail message can consist of short text strings, the output from a query, or an attached file.
精读是在较短文本中用于获取具体信息的一种阅读技巧。
Intensive reading is used on shorter texts in order to extract specific information.
实验表明：利用上下位关系能够改善短文本的分类性能。
The experimental results show that short-text classification performance can be improved by using the hyponymy.
单独把依存关系作为特征，不能提高短文本的分类性能；
Using dependency relation to classify short texts lonely can not improve the classification performance;
这些自定义字段就是短文本字段，并且最大只能显示400个字节的文本。
The custom fields are short text fields that can only display a maximum of 400 bytes of text.
该函数返回一个表，其中包含模型中的集群，以及关于这个集群中字段分布的简短文本描述。
DM_GETCLUSTERS that returns a table of the clusters in the model together with a short text description about the field distribution in this cluster.
本文主要研究了基于内容的垃圾短信过滤，它可以看成是一个不规则短文本的分类问题。
In this thesis, the main research is content-based junk short messages filtering which can be treated as irregular short text classification problem.
实验表明，基于该算法的聚类系统对于大量的变异短文本有着很高的执行效率和准确率。
Experiments show that the clustering system based on this algorithm can depose lots of abnormal short texts with high accuracy...
如前所述，集群信息包括对集群的简短文本描述(在这里就是对这个集群中所有离群值记录的描述)和其他信息。
As described above, the cluster information contains, among others, a short text description of the cluster (which is, in this case, a description of all outlier records in this cluster).
然而，中文网络短文本固有的关键词词频低、存在大量变形词等特点，使得难以直接使用现有面向长文本的聚类算法。
Since Chinese network short text is less of keywords and full of anomalous writings, the traditional text clustering method is not directly suitable for network short text clustering.
实验表明，基于该算法的聚类系统对于大量的变异短文本处理速度可以达到每小时百万级以上，并且有比较高的准确率。
Experiments show that the clustering system based on this algorithm can depose millions of abnormal short texts per hour with high accuracy.
而且使用基于本体的短文本分类方法，无须训练样本，可以通过本体获得语义信息并结合相似性计算来实现对短文本的自动分类。
Without training samples when using this method, we can get semantic information of ontology and combine the similarity calculations to achieve the short-text classification.
之后，基于提取的计算机领域术语构建了计算机领域语的层次结构，并结合了短文本语义比较的方法，最终形成计算机图书的推荐。
Then a level structure was built with the domain words just extracting. At last, a recommendation method is proposed combine with short text comparison arithmetic.
观察文本的数量及其形态——是很长的文章，还是很多篇短文，或是长短混杂。
Look at both amount of text and how it is broken down - long articles, lots of short articles, a mix of long and short articles.
观察文本的数量及其形态——是很长的文章，还是很多篇短文，或是长短混杂。
Look at both amount of text and how it is broken down - long articles, lots of short articles, a mix of long and short articles.
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