本文将阅卷过程分解为四个主要任务来进行的：分句分词、句法分析、关键词提取和相似度计算。
We divide the system into four main tasks: word segment and sentence segment, syntax analysis, keyword extraction and sentence similarity computing.
Tellefsen 称“NetBase并不使用关键词或者基本词语对数以亿计的文档进行搜索，相反NetBase实际上能够阅读并提取整个句子和概念中的语义。”
Says Tellefsen, "Rather than using keywords or basic entities to search through billions of documents, NetBase can actually read and extract linguistic meaning from entire sentences and concepts."
Peerset从数百万计的网站、档案、状态消息中提取数据，并整理成一系列关键词簇。
Peerset scrapes data from millions of sites, profiles and status messages and compiles a series of word clusters.
本文中使用的方法是从整个平面文件数据库中指定的字段中提取所有单个关键词。
The approach used in this article is to extract all of the individual words from a specified field in the entire flat-file database.
Peerset的定位算法（targetingalgorithm）不同于追踪算法（dating algorithm），定位算法从网络档案中提取关键词和元数据信息，然后和相关信息进行匹配。
Not unlike dating algorithms, Peerset's targeting algorithm takes keywords and meta data from online profiles and matches them with relevant information.
在检索特定领域信息时，通过相关样本集融合，提取出关键词集，通过调节样本集实现关键词集的柔性控制，以调控搜索空间与结果取向。
When searching special area information, it fuses sample documents, extracts common keywords, and adjusts the sample documents to control the search space and result sum.
在全面搜集整理学术类茶科技文献的基础上，从8752篇文献中提取有效关键词。
Based on the comprehensively collection and coordination of academic literatures on tea science, all of the key words in 8752 academic tea literatures were picked up.
利用聊天双方的聊天信息来提供更好的服务成为研究者们的重要课题，而如何提取聊天文本中的关键词又成为此类研究的重点。
How to take advantage of chat text to serve people and how to extract key words from this text has attracted more and more researchers.
利用聊天双方的聊天信息来提供更好的服务成为研究者们的重要课题，而如何提取聊天文本中的关键词又成为此类研究的重点。
How to take advantage of chat text to serve people and how to extract key words from this text has attracted more and more researchers.
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