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Information Extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis,  IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events  in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to “understand” an attack article only enough to find data corresponding to the slots in this template.

Information Extraction is the part of a greater puzzle which deals with the problem of devising automatic methods for text management, beyond its transmission, storage and display. The discipline of information retrieval (IR) has developed automatic methods, typically of a statistical flavor, for indexing large document collections and classifying documents. Another complementary approach is that of natural language processing (NLP) which has solved the problem of modelling human language processing with considerable success when taking into account the magnitude of the task. In terms of both difficulty and emphasis,  IE deals with tasks in between both IR and NLP. In terms of input, IE assumes the existence of a set of documents in which each document follows a template, i.e. describes one or more entities or events  in a manner that is similar to those in other documents but differing in the details. An example, consider a group of newswire articles on Latin American terrorism with each article presumed to be based upon one or more terroristic acts. We also define for any given IE task a template, which is a(or a set of) case frame(s) to hold the information contained in a single document. For the terrorism example, a template would have slots corresponding to the perpetrator, victim, and weapon of the terroristic act, and the date on which the event happened. An IE system for this problem is required to “understand” an attack article only enough to find data corresponding to the slots in this template.

信息抽取是一个较为上游的任务。它涉及的问题是设计文本管理的自动方法，不再局限于文本的传输，存储和显示。信息检索学科已经开发出了自动化的方法，典型的统计方法，用于为大型文档集合建立索引和对文档进行分类。另一个互补的方法是自然语言处理(NLP) ，它解决了人类语言处理建模的问题，在处理大规模任务时取得了相当的成功。就难度和重点而言，信息抽取(Information Extraction)处理介于信息获取（Information Retrieval,IR）和 NLP 之间的任务。对于IE任务的输入假设为，一组文档，其中每个文档都遵循一个模板，即，以类似于其他文档中的方式描述一个或多个实体或事件，但在细节上有所不同。例如，考虑一组关于拉丁美洲恐怖主义的新闻专线文章，每一条都被认为是基于一种或多种恐怖主义行为。我们还为任何给定的 IE 任务定义了一个模板，它是一个(或一组)案例框架，用于保存单个文档中包含的信息。对于恐怖主义的例子，一个模板应该有与恐怖主义行为的肇事者、受害者和武器相对应的位置，以及事件发生的日期。针对这个问题的 IE 系统需要“理解”一篇攻击文章，只要找到与此模板中插槽相对应的数据即可。

信息抽取是一个较为上游的任务。它涉及的问题是设计文本管理的自动方法，不再局限于文本的传输，存储和显示。信息检索学科已经开发出了自动化的方法，典型的统计方法，用于为大型文档集合建立索引和对文档进行分类。另一个互补的方法是自然语言处理(NLP) ，它解决了人类语言处理建模的问题，在处理大规模任务时取得了相当的成功。就难度和重点而言，信息抽取(Information Extraction)处理介于信息获取（Information Retrieval,IR）和 NLP 之间的任务。对于IE任务的输入假设为，一组文档，其中每个文档都遵循一个模板，即，以类似于其他文档中的方式描述一个或多个实体或事件，但在细节上有所不同。例如，考虑一组关于拉丁美洲恐怖主义的新闻专线文章，每一条都被认为是基于一种或多种恐怖主义行为。我们还为任何给定的 IE 任务定义了一个模板，它是一个(或一组)案例框架，用于保存单个文档中包含的信息。对于恐怖主义的例子，一个模板应该有与恐怖主义行为的肇事者、受害者和武器相对应的位置，以及事件发生的日期。针对这个问题的 IE 系统需要“理解”一篇关于恐怖袭击的文章，找到与此模板中角色相对应的数据。

==History==

==History==

*MUC-7 (1998): Satellite launch reports.

*MUC-7 (1998): Satellite launch reports.

从1987年开始，IE 受到了一系列信息理解会议的激励。是一个以竞争为基础的会议，该会议由巴西科斯坦蒂诺大学金融信息抽取 Paolo Coletti 主办，Wit 出版社，2008年。主要关注以下领域:  

从1987年开始，一系列信息理解会议加速着信息抽取任务的发展。MUC是一个基于竞赛的会议，其主要关注以下领域:  

* MUC-1(1987) ，MUC-2(1989) : 海军行动信息。

* MUC-1(1987) ，MUC-2(1989) : 海军行动信息。

* MUC-3(1991) ，MUC-4(1992) : 拉丁美洲国家的恐怖主义。

* MUC-3(1991) ，MUC-4(1992) : 拉丁美洲国家的恐怖主义。

The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the world wide web, refers to the existing Internet as the web of documents  and advocates that more of the content be made available as a web of data.  Until this transpires, the web largely consists of unstructured documents lacking semantic metadata.  Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags.  An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with.  A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted.R. K. Srihari, W. Li, C. Niu and T. Cornell,"InfoXtract: A Customizable Intermediate Level Information Extraction Engine",Journal of Natural Language Engineering, Cambridge U. Press, 14(1), 2008, pp.33-69.

The present significance of IE pertains to the growing amount of information available in unstructured form. Tim Berners-Lee, inventor of the world wide web, refers to the existing Internet as the web of documents  and advocates that more of the content be made available as a web of data.  Until this transpires, the web largely consists of unstructured documents lacking semantic metadata.  Knowledge contained within these documents can be made more accessible for machine processing by means of transformation into relational form, or by marking-up with XML tags.  An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with.  A typical application of IE is to scan a set of documents written in a natural language and populate a database with the information extracted.R. K. Srihari, W. Li, C. Niu and T. Cornell,"InfoXtract: A Customizable Intermediate Level Information Extraction Engine",Journal of Natural Language Engineering, Cambridge U. Press, 14(1), 2008, pp.33-69.

IE 目前的重要意义在于以非结构化的形式获得越来越多的信息。万维网的发明者 Tim Berners-Lee 将现有的互联网称为文档网络，并主张更多的内容以数据网络的形式提供。在此之前，网络大部分是由缺乏语义元数据的非结构化文档组成的。这些文档中包含的知识可以通过转换为关系形式或使用 XML 标记使机器处理更容易访问。一个监控新闻数据源的智能代理需要 IE 将非结构化数据变成可以理解的东西。IE 的一个典型应用程序是扫描一组用自然语言编写的文档，并用提取的信息填充数据库。牛和康奈尔，《在 foxtract: 一个可定制的中级信息抽取引擎》 ，《自然语言工程杂志》 ，剑桥大学出版社。按14(1) ，2008，pp. 33-69。

在于以非结构化信息日益增多的时代，信息抽取的意义也愈发重大。万维网的发明者 Tim Berners-Lee 将现有的互联网称为文档网络，并主张更多的内容以数据网络的形式提供。在此之前，网络大部分是由缺乏语义元数据的非结构化文档组成的。这些文档中包含的知识可以通过转换为关系形式或使用 XML 标记使机器更容易处理和访问。一个监控新闻数据源的智能体需要具备信息抽取能力将非结构化数据变成可用于下游任务推理的结构化信息。I信息抽取的一个典型应用程序是扫描一组用自然语言编写的文档，并用提取的信息填充数据库。

==Tasks and subtasks==

==Tasks and subtasks==

Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include:

Applying information extraction to text is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical IE tasks and subtasks include:

将信息抽取应用于文本是与文本简化问题联系在一起的，以便创建一个自由文本信息的结构化视图。总体目标是创建一个更容易机器阅读的文本来处理句子。典型的 IE 任务和子任务包括:

将信息抽取应用于文本是与文本简化问题联系在一起的，以便创建一个自由文本信息的结构化视图。总体目标是创建一个更容易机器阅读的文本来处理句子。典型的信息抽取任务和子任务包括:

* Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack.

* Template filling: Extracting a fixed set of fields from a document, e.g. extract perpetrators, victims, time, etc. from a newspaper article about a terrorist attack.

*  

*  

* 事件提取: 给定一个输入文档，输出零个或多个事件模板。例如，一篇报纸文章可能描述了多起恐怖袭击。

* 事件提取: 给定一个输入文档，输出零个或多个事件模板。例如，一篇报纸文章可能描述了多起恐怖袭击。

* 知识库人口: 填充给定一组文件的事实数据库。通常数据库是三元组的形式，例如: 实体1，关系，实体2。命名实体识别: 利用现有的领域知识或从其他句子中提取的信息，识别已知的实体名称(用于人和组织)、地名、时间表达式和某些类型的数字表达式。一般来说，识别任务需要将一个唯一标识符分配给提取的实体。一个简单的任务是命名实体检测，其目的是检测实体没有任何实体实例的现有知识。例如，在处理”史密斯先生喜欢捕鱼”一句时，命名实体检测将表示检测到”史密斯先生”一词确实指的是一个人，但不一定了解(或使用)某个史密斯先生，他就是(或”可能是”)该句所指的具体人。

* 知识库填充: 填充给定一组文件的事实数据库。通常数据库是三元组的形式，例如: 实体1，关系，实体2。

* 命名实体识别: 利用现有的领域知识或从其他句子中提取的信息，识别已知的实体名称(用于人和组织)、地名、时间表达式和某些类型的数字表达式。一般来说，识别任务需要将一个唯一标识符分配给提取的实体。一个简单的任务是命名实体检测，其目的是检测实体没有任何实体实例的现有知识。例如，在处理”史密斯先生喜欢捕鱼”一句时，命名实体检测将表示检测到”史密斯先生”一词确实指的是一个人，但不一定了解(或使用)某个史密斯先生，他就是(或”可能是”)该句所指的具体人。

*  

*  

* 共指消解: 检测文本实体之间的共指和回指链接。在 IE 任务中，这通常局限于查找以前提取的命名实体之间的链接。例如，“ International Business Machines”和“ IBM”指的是相同的实际实体。如果我们把这两个句子取为“史密斯先生喜欢钓鱼。但是他不喜欢骑自行车”，如果能够发现“他”指的是先前被发现的人“ m · 史密斯”，那就更好了。

* 共指消解: 检测文本实体之间的共指和回指链接。在 IE 任务中，这通常局限于查找以前提取的命名实体之间的链接。例如，“ International Business Machines”和“ IBM”指的是相同的实际实体。如果我们把这两个句子取为“史密斯先生喜欢钓鱼。但是他不喜欢骑自行车”，如果能够发现“他”指的是先前被发现的人“ m · 史密斯”，那就更好了。

*  

*  

*  

*  

* 位于位置的人(摘自“ Bill is in France.”一句)

* 半结构化信息抽取，它是试图恢复某种信息结构的信息抽取方法的统称，这种信息结构在发布过程中已经丢失，例如:

* 半结构化信息抽取，它可能指的是任何试图恢复某种信息结构的 IE，这种信息结构在发布过程中已经丢失，例如:  

*  

*  

* 表提取: 从文档中查找和提取表。

* 表提取: 从文档中查找和提取表。

Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE.

Note that this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE.

请注意，这一清单并非详尽无遗，而且普遍不接受 IE 活动的确切含义，许多方法将 IE 的多个子任务结合起来，以实现更广泛的目标。IE 中经常使用机器学习、统计分析和/或自然语言处理。

请注意，这一清单并非详尽无遗，而且信息抽取的并没有一个准确的定义，许多方法将 IE 的多个子任务结合起来，以实现更广泛的目标。IE 中经常使用机器学习、统计分析和/或自然语言处理。

IE on non-text documents is becoming an increasingly interesting topic{{when|date=March 2017}} in research, and information extracted from multimedia documents can now{{when|date=March 2017}} be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources.

IE on non-text documents is becoming an increasingly interesting topic{{when|date=March 2017}} in research, and information extracted from multimedia documents can now{{when|date=March 2017}} be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources.

IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources.

IE on non-text documents is becoming an increasingly interesting topic in research, and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally leads to the fusion of extracted information from multiple kinds of documents and sources.

==World Wide Web applications==

==World Wide Web applications==

IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people to cope with the enormous amount of data that is available online. Systems that perform IE from online text should meet the requirements of low cost, flexibility in development and easy adaptation to new domains. MUC systems fail to meet those criteria. Moreover, linguistic analysis performed for unstructured text does not exploit the HTML/XML tags and the layout formats that are available in online texts. As a result, less linguistically intensive approaches have been developed for IE on the Web using wrappers, which are sets of highly accurate rules that extract a particular page's content. Manually developing wrappers has proved to be a time-consuming task, requiring a high level of expertise. Machine learning techniques, either supervised or unsupervised, have been used to induce such rules automatically.

IE has been the focus of the MUC conferences. The proliferation of the Web, however, intensified the need for developing IE systems that help people to cope with the enormous amount of data that is available online. Systems that perform IE from online text should meet the requirements of low cost, flexibility in development and easy adaptation to new domains. MUC systems fail to meet those criteria. Moreover, linguistic analysis performed for unstructured text does not exploit the HTML/XML tags and the layout formats that are available in online texts. As a result, less linguistically intensive approaches have been developed for IE on the Web using wrappers, which are sets of highly accurate rules that extract a particular page's content. Manually developing wrappers has proved to be a time-consuming task, requiring a high level of expertise. Machine learning techniques, either supervised or unsupervised, have been used to induce such rules automatically.

IE 已经成为 MUC 会议的焦点。然而，随着互联网的普及，人们更加需要开发 IE 系统，以帮助人们处理在线可用的大量数据。从在线文本执行 IE 的系统应该满足低成本、开发灵活性和易于适应新领域的要求。MUC 系统不能满足这些标准。此外，对非结构化文本执行的语言分析并没有利用 HTML/XML 标记和在线文本中可用的布局格式。因此，使用包装器为 IE 开发了语言密集度较低的方法，这些包装器是一组高度精确的规则，可以提取特定页面的内容。事实证明，手动开发包装器是一项耗时的任务，需要高水平的专业知识。机器学习技术，无论是监督或无监督，已被用来归纳这些规则自动。

信息抽取已经是MUC 会议的焦点。然而，随着互联网的普及，人们更加需要开发能够帮助人们处理大规模在线数据的信息抽取系统。从在线文本执行 IE 的系统应该满足低成本、开发灵活性和易于适应新领域的要求。MUC 系统不能满足这些标准。此外，对非结构化文本执行的语言分析并没有利用 HTML/XML 标记和在线文本中可用的布局格式。因此，使用包装器为 IE 开发了不依赖于语言学分析的方法，这些包装器是一组高度精确的规则，可以提取特定页面的内容。事实证明，手动开发包装器是一项耗时的任务，需要高水平的专业知识。机器学习技术，无论是监督或无监督，已被用来自动归纳这些规则。

''Wrappers'' typically handle highly structured collections of web pages, such as product catalogs and telephone directories. They fail, however, when the text type is less structured, which is also common on the Web. Recent effort on ''adaptive information extraction'' motivates the development of IE systems that can handle different types of text, from well-structured to almost free text -where common wrappers fail- including mixed types. Such systems can exploit shallow natural language knowledge and thus can be also applied to less structured texts.

''Wrappers'' typically handle highly structured collections of web pages, such as product catalogs and telephone directories. They fail, however, when the text type is less structured, which is also common on the Web. Recent effort on ''adaptive information extraction'' motivates the development of IE systems that can handle different types of text, from well-structured to almost free text -where common wrappers fail- including mixed types. Such systems can exploit shallow natural language knowledge and thus can be also applied to less structured texts.

Wrappers typically handle highly structured collections of web pages, such as product catalogs and telephone directories. They fail, however, when the text type is less structured, which is also common on the Web. Recent effort on adaptive information extraction motivates the development of IE systems that can handle different types of text, from well-structured to almost free text -where common wrappers fail- including mixed types. Such systems can exploit shallow natural language knowledge and thus can be also applied to less structured texts.

Wrappers typically handle highly structured collections of web pages, such as product catalogs and telephone directories. They fail, however, when the text type is less structured, which is also common on the Web. Recent effort on adaptive information extraction motivates the development of IE systems that can handle different types of text, from well-structured to almost free text -where common wrappers fail- including mixed types. Such systems can exploit shallow natural language knowledge and thus can be also applied to less structured texts.

Wrappers 通常处理高度结构化的网页集合，如产品目录和电话目录。然而，当文本类型结构化程度较低时，它们就会失败，这在 Web 上也很常见。最近在自适应信息抽取方面的努力促进了 IE 系统的发展，该系统可以处理不同类型的文本，从结构良好的到几乎是自由的文本——这是通常的包装器失败的地方——包括混合类型。这样的系统可以利用浅层的自然语言知识，因此也可以应用于结构化程度较低的文本。

Wrappers 通常处理高度结构化的网页，如产品目录和电话目录。然而，当文本类型结构化程度较低时，它们就会失败，这在 Web 上也很常见。最近在自适应信息抽取方面的研究取得了进展，这些系统可以处理不同类型的文本，从结构良好的到几乎完全无结构自由书写的文本——这是通常的包装器无法处理的信息——包括混合类型。这样的系统利用浅层的自然语言知识，因此也可以应用于结构化程度较低的文本。

A recent{{when|date=March 2017}} development is Visual Information Extraction,<ref>{{cite arXiv|eprint = 1506.08454|title=WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Information Extraction|first1=Vijil  |last1=Chenthamarakshan|first2=Prasad M |last2=Desphande |first3= Raghu |last3=Krishnapuram |first4= Ramakrishnan |last4=Varadarajan |first5= Knut |last5=Stolze|year=2015|class=cs.CL}}</ref><ref>{{cite document|citeseerx = 10.1.1.21.8236|title=Visual Web Information Extraction with Lixto|first1=Robert  |last1=Baumgartner|first2=Sergio |last2=Flesca |first3= Georg |last3=Gottlob|year=2001|pages=119–128}}</ref> that relies on rendering a webpage in a browser and creating rules based on the proximity of regions in the rendered web page. This helps in extracting entities from complex web pages that may exhibit a visual pattern, but lack a discernible pattern in the HTML source code.

A recent{{when|date=March 2017}} development is Visual Information Extraction,<ref>{{cite arXiv|eprint = 1506.08454|title=WYSIWYE: An Algebra for Expressing Spatial and Textual Rules for Information Extraction|first1=Vijil  |last1=Chenthamarakshan|first2=Prasad M |last2=Desphande |first3= Raghu |last3=Krishnapuram |first4= Ramakrishnan |last4=Varadarajan |first5= Knut |last5=Stolze|year=2015|class=cs.CL}}</ref><ref>{{cite document|citeseerx = 10.1.1.21.8236|title=Visual Web Information Extraction with Lixto|first1=Robert  |last1=Baumgartner|first2=Sergio |last2=Flesca |first3= Georg |last3=Gottlob|year=2001|pages=119–128}}</ref> that relies on rendering a webpage in a browser and creating rules based on the proximity of regions in the rendered web page. This helps in extracting entities from complex web pages that may exhibit a visual pattern, but lack a discernible pattern in the HTML source code.

A recent development is Visual Information Extraction, that relies on rendering a webpage in a browser and creating rules based on the proximity of regions in the rendered web page. This helps in extracting entities from complex web pages that may exhibit a visual pattern, but lack a discernible pattern in the HTML source code.

A recent development is Visual Information Extraction, that relies on rendering a webpage in a browser and creating rules based on the proximity of regions in the rendered web page. This helps in extracting entities from complex web pages that may exhibit a visual pattern, but lack a discernible pattern in the HTML source code.

最近的一个发展是 Visual 信息抽取，它依赖于在浏览器中渲染网页，并根据渲染网页中区域的接近程度创建规则。这有助于从复杂的网页中提取实体，这些网页可能表现出一种视觉模式，但在 HTML 源代码中缺乏一种可识别的模式。

最近的一个发展是基于视觉信息的信息抽取，它依赖于在浏览器中渲染网页，并根据渲染网页中区域的接近程度创建规则。这有助于从复杂的网页中提取实体，这些网页可能表现出一种视觉模式，但在 HTML 源代码中缺乏一种可识别的模式。

==Approaches==

==Approaches==

* 文本工程通用体系结构(GATE)与免费信息抽取系统捆绑在一起

* 文本工程通用体系结构(GATE)捆绑了一个免费信息抽取系统

* Apache OpenNLP 是一个用于自然语言处理的 Java 机器学习工具包  

* Apache OpenNLP 是一个用于自然语言处理的 Java 机器学习工具包  

* OpenCalais 是来自 Thomson Reuters 的一个自动化的信息抽取网络服务(免费限制版本)  

* OpenCalais 是来自 Thomson Reuters 的一个自动化的信息抽取网络服务(免费限制版本)