A new algorithm based on hidden Markov Model is proposed for text information extraction.
提出了一种基于隐马尔可夫模型的文本信息抽取算法。
This thesis mainly studies relative algorithms on text information extraction based on wrapper model.
本文研究基于包装器模型的文本信息抽取算法。
The algorithm makes use of the information of format and list separators to segment text, and then combines hidden Markov model for text information extraction.
该算法利用文本排版格式、分隔符等信息，对文本进行分块，在分块的基础上结合隐马尔可夫模型进行文本信息抽取。
Absrtact: As a branch of natural language processing, the extraction of useful information in large text, the text information extraction became an important means.
摘要：作为自然语言处理的一个分支，文本信息抽取成为了提取大量文本信息中有用信息的重要手段。
Text information extraction is an important method of processing large quantity of text. The application of hidden Markov model to information extraction is a relatively new research topic.
文本信息抽取是处理海量文本的重要手段，将隐马尔可夫模型应用到信息抽取领域是一个比较新的研究课题。
This means they easily lend themselves to specifying information extraction results. For example, in the following text a Company annotation would cover positions 19 to 21.
这意味着可以方便地使用注释指定信息提取结果。
Text extraction is a key technique in content-based video retrieval as textual information plays an important role in describing the content of image.
文字信息在描述图象内容时起着重要的作用，因此文字提取及识别是基于内容视频检索的关键技术。
Information extraction is a main approach for constructing database from free text corpus and for automatic collecting intelligence information.
信息抽取是从自由文本语料库构建数据库，实现情报自动收集的有效途径之一。
Information extraction refers to the task of extracting information from a text in the form of text strings which are placed into slots labeled to indicate the kind of information that can fill them.
信息抽取的任务是从文本中抽取字符串形式的信息，并将此信息填入带标记的槽中，来表明其含义。
A sign has various kinds of important information and therefore, sign text extraction in natural scene images is very useful in many applications.
标志牌上包含了许多重要的信息，提取自然场景图像中标志牌上的文本具有很高的实用价值。
Text feature extraction and text categorization is the focal point of basic research in the field of intelligent information service system.
文本信息特征提取和文本分类是当前智能信息服务系统基础研究的重点。
Text categorization and feature concept extraction is the focal point of basic research in the field of intelligent information service system.
文本分类与文本信息特征概念的提取是当前智能信息服务研究的重点。
The analysis of special pages and text extraction methods in this paper has a practical significance in the research of web information technology and the application of networks.
文中对特殊网页的分析及其文本提取方法的研究，对网页信息挖掘技术研究和网络应用、网络监察具有重要的实际意义。
To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed.
针对词、潜在概念、文本和主题之间的模糊关系，提出一种基于信息论的潜在概念获取与文本聚类方法。
To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed.
针对词、潜在概念、文本和主题之间的模糊关系，提出一种基于信息论的潜在概念获取与文本聚类方法。
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