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此词条暂由彩云小译翻译，翻译字数共312，未经人工整理和审校，带来阅读不便，请见谅。

{{Multiple issues|

{{Multiple issues|

{多个问题 |

{{citation style|date=December 2011}}

{{technical|date=October 2012}}

{{abbreviations|date=October 2012}}

}}

}}

}}

'''Automatic content extraction''' ('''ACE''') is a research program for developing advanced [[information extraction]] [[technologies]] convened by the [[National Institute of Standards and Technology|NIST]] from 1999 to 2008, succeeding [[Message Understanding Conference|MUC]] and preceding [https://www.nist.gov/tac/ Text Analysis Conference].

Automatic content extraction (ACE) is a research program for developing advanced information extraction technologies convened by the NIST from 1999 to 2008, succeeding MUC and preceding [https://www.nist.gov/tac/ Text Analysis Conference].

自动内容提取(ACE)是一个研究项目，目的是开发先进的信息抽取技术，由 NIST 在1999年至2008年间召开，接替 MUC 和之前的[ https://www.NIST.gov/tac/文本分析会议]。

==Goals and efforts==

In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations, and the events) rather than in terms of the words in the text. For example, the so-called "named entity" task, as defined in MUC, is to identify those words (on the page) that are names of entities. In ACE, on the other hand, the corresponding task is to identify the entity so named. This is a different task, one that is more abstract and that involves inference more explicitly in producing an answer. In a real sense, the task is to detect things that "aren't there".

In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations, and the events) rather than in terms of the words in the text. For example, the so-called "named entity" task, as defined in MUC, is to identify those words (on the page) that are names of entities. In ACE, on the other hand, the corresponding task is to identify the entity so named. This is a different task, one that is more abstract and that involves inference more explicitly in producing an answer. In a real sense, the task is to detect things that "aren't there".

总的来说，ACE 程序的动机和解决的问题与之前的 MUC 程序相同。然而，ACE 程序以目标对象(即实体、关系和事件)来定义研究目标，而不是以文本中的词语来定义。例如，在 MUC 中定义的所谓“命名实体”任务就是识别(页面上的)实体名称的单词。另一方面，在 ACE 中，相应的任务是标识如此命名的实体。这是一个不同的任务，一个更抽象的任务，在产生答案时涉及更明确的推理。在真正意义上，任务是检测“不存在”的东西。

While the ACE program is directed toward extraction of information from [[Sound|audio]] and [[image]] sources in addition to pure text, the research effort is restricted to information extraction from text. The actual [[transduction (machine learning)|transduction]] of audio and image data into text is not part of the ACE research effort, although the processing of [[Speech recognition | ASR]] and [[Optical character recognition | OCR]] output from such transducers is.

While the ACE program is directed toward extraction of information from audio and image sources in addition to pure text, the research effort is restricted to information extraction from text. The actual transduction of audio and image data into text is not part of the ACE research effort, although the processing of ASR and OCR output from such transducers is.

虽然 ACE 项目的目标是从音频和图像资源中提取除纯文本以外的信息，但研究工作仅限于从文本中提取信息抽取。实际将音频和图像数据转换成文本并不是 ACE 研究工作的一部分，尽管这些转换器输出的 ASR 和 OCR 信号的处理是 ACE 研究工作的一部分。

The effort involves:

The effort involves:

这些努力包括:

* defining the research tasks in detail,

* collecting and annotating data needed for training, development, and evaluation,

* supporting the research with evaluation tools and [[research workshop]]s.

==Topics and exercises==

Given a text in [[natural language]], the ACE challenge is to detect:

Given a text in natural language, the ACE challenge is to detect:

给定一个自然语言的文本，ACE 面临的挑战是检测:

# '''entities''' mentioned in the text, such as: persons, organizations, locations, facilities, weapons, vehicles, and geo-political entities.

entities mentioned in the text, such as: persons, organizations, locations, facilities, weapons, vehicles, and geo-political entities.

文中提到的实体，例如: 个人、组织、地点、设施、武器、车辆和地缘政治实体。

# '''relations''' between entities, such as: person A is the manager of company B. Relation types include: role, part, located, near, and social.

relations between entities, such as: person A is the manager of company B. Relation types include: role, part, located, near, and social.

实体之间的关系，例如: a 是 b 公司的经理。关系类型包括: 角色、部分、位置、接近和社会。

# '''events''' mentioned in the text, such as: interaction, movement, transfer, creation and destruction.

events mentioned in the text, such as: interaction, movement, transfer, creation and destruction.

文本中提到的事件，如: 相互作用，运动，转移，创造和破坏。

The program relates to [[English language|English]], [[Arabic language|Arabic]] and [[Chinese language|Chinese]] texts.

The program relates to English, Arabic and Chinese texts.

该计划涉及英语，阿拉伯语和中文文本。

The ACE corpus is one of the standard benchmarks for testing new information extraction [[algorithm]]s.

The ACE corpus is one of the standard benchmarks for testing new information extraction algorithms.

ACE 语料库是测试新的信息抽取算法的标准基准之一。

==References==

* George Doddington@NIS T, Alexis Mitchell@LD C, Mark Przybocki@NIS T, Lance Ramshaw@BB N, Stephanie Strassel@LD C, Ralph Weischedel@BB N. [https://web.archive.org/web/20150126022215/http://www.citeulike.org/user/erelsegal-halevi/article/10003935 The automatic content extraction (ACE) program–tasks, data, and evaluation.] 2004

==External links==

* [http://www.itl.nist.gov/iaui/894.02/related_projects/muc/ MUC] - ACE's predecessor.

* [http://projects.ldc.upenn.edu/ace/ ACE] {{Webarchive|url=https://web.archive.org/web/20130925194420/http://projects.ldc.upenn.edu/ace/ |date=2013-09-25 }} (LDC)

* [https://web.archive.org/web/20060308054306/http://www.itl.nist.gov/iad/894.01/tests/ace/ ACE] (NIST)

[[Category:Information retrieval organizations]]

Category:Information retrieval organizations

类别: 信息检索组织

<noinclude>

<small>This page was moved from [[wikipedia:en:Automatic content extraction]]. Its edit history can be viewed at [[自动内容提取/edithistory]]</small></noinclude>

[[Category:待整理页面]]

@@ 第40行： / 第40行： @@
 ==外部链接==
 * [http://www.itl.nist.gov/iaui/894.02/related_projects/muc/ MUC] - ACE's predecessor.
-* [http://projects.ldc.upenn.edu/ace/ ACE] {{Webarchive|url=https://web.archive.org/web/20130925194420/http://projects.ldc.upenn.edu/ace/ |date=2013-09-25 }} (LDC)
+* [http://projects.ldc.upenn.edu/ace/ ACE] (LDC)
 * [https://web.archive.org/web/20060308054306/http://www.itl.nist.gov/iad/894.01/tests/ace/ ACE] (NIST)
 ==编者推荐==

@@ 第1行： / 第1行： @@
-此词条暂由彩云小译翻译，翻译字数共388，人工整理：林家驹。
+{{#seo:
-'''Automatic content extraction''' ('''ACE''') is a research program for developing advanced [[information extraction]] [[technologies]] convened by the [[National Institute of Standards and Technology|NIST]] from 1999 to 2008, succeeding [[Message Understanding Conference|MUC]] and preceding [https://www.nist.gov/tac/ Text Analysis Conference].
+'''自动内容提取 Automatic content extraction（ACE）'''是由美国国家标准与技术研究所（NIST）在1999年至2008年间召开的一个研究项目，目的是开发先进的[[信息抽取]]/文本分析技术，后续还有信息理解会议（MUC）和之前的文本分析会议。
-自动内容提取(ACE)是由 NIST 在1999年至2008年间召开的一个研究项目，目的是开发先进的信息抽取/文本分析技术，后续还有 MUC 和之前的文本分析会议。
+==目标==
-总的来说，ACE 程序的动机和解决的问题与之前的 MUC 程序相同。然而，ACE 程序以目标对象(即实体、关系和事件)来定义研究目标，而不是以文本中的词语来定义。例如，在 MUC 中定义的所谓“命名实体”任务就是识别(页面上的)实体名称的单词。而在 ACE 中，相应的任务是标识如此命名的实体。后一个任务更加抽象，在产生答案时涉及更明确的推理。从某种意义上说，AUC检测的实体“并不在那里”。
+虽然 ACE 项目的目标是从音频和图像资源中提取除纯文本以外的信息，但研究工作仅限于从文本中提取信息抽取。实际将音频和图像数据转换成文本并不是 ACE 研究工作的一部分，不过这些转换器输出的语音识别（ASR）和光学字符识别（OCR）的输出是 ACE 研究工作的一部分。
 这项工作包括:
-* 详细定义研究任务，
+* 详细定义研究任务；
-* 收集和注释数据集，包括训练集测试集等，
+* 收集和注释数据集，包括训练集测试集等；
 * 通过评估工具和研究工作坊支持研究。
 ==涉及话题与具体内容==
 给定一个自然语言的文本，ACE 的挑战是检测: 文本中提到的
-<nowiki>#</nowiki> 实体，如: 个人、组织、地点、设施、武器、车辆和地理政治实体。
+#实体，如: 个人、组织、地点、设施、武器、车辆和地理政治实体。
 #实体之间的关系，例如: a 人是 b 公司的经理。关系类型包括: 角色、部分、位置、接近和社会。
-<nowiki>#</nowiki> 实体之间的关系，例如: a 人是 b 公司的经理。关系类型包括: 角色、部分、位置、接近和社会。
+#文本中提到的事件，比如: 互动，运动，转移，创造和毁灭。
 该计划涉及英语，阿拉伯语和中文文本。
 ACE 语料库是测试新的信息抽取算法的标准基准之一。
 ==参考资料==
 * George Doddington@NIS T, Alexis Mitchell@LD C, Mark Przybocki@NIS T, Lance Ramshaw@BB N, Stephanie Strassel@LD C, Ralph Weischedel@BB N. [https://web.archive.org/web/20150126022215/http://www.citeulike.org/user/erelsegal-halevi/article/10003935 The automatic content extraction (ACE) program–tasks, data, and evaluation.] 2004
 ==外部链接==
@@ 第54行： / 第43行： @@
 * [https://web.archive.org/web/20060308054306/http://www.itl.nist.gov/iad/894.01/tests/ace/ ACE] (NIST)
-* MUC - ACE's predecessor.
+==编者推荐==
-* ACE  (LDC)
+===集智课程===
-* ACE (NIST)
+[[File:产学结合_爱奇艺.jpg|right|thumb|300px]]
-* MUC-ACE 的前身。
+====[https://campus.swarma.org/course/807 产学结合：自然语言处理及其应用]====
-* ACE  (LDC)
+本课程中，讲解自然语言处理的最新学术理论与业界实践，涉及语言模型、机器翻译、情感分析、文本理解、文本生成五大主题。
-Category:Information retrieval organizations
+----
-<small>This page was moved from [[wikipedia:en:Automatic content extraction]]. Its edit history can be viewed at [[自动内容提取/edithistory]]</small></noinclude>
+'''本词条内容源自wikipedia及公开资料，遵守 CC3.0协议。'''
-[[Category:待整理页面]]
+[[Category:信息检索组织]]

@@ 第18行： / 第18行： @@
 In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations, and the events) rather than in terms of the words in the text. For example, the so-called "named entity" task, as defined in MUC, is to identify those words (on the page) that are names of entities. In ACE, on the other hand, the corresponding task is to identify the entity so named. This is a different task, one that is more abstract and that involves inference more explicitly in producing an answer. In a real sense, the task is to detect things that "aren't there".
-总的来说，ACE 程序的动机和解决的问题与之前的 MUC 程序相同。然而，ACE 程序以目标对象(即实体、关系和事件)来定义研究目标，而不是以文本中的词语来定义。例如，在 MUC 中定义的所谓“命名实体”任务就是识别(页面上的)实体名称的单词。另一方面，在 ACE 中，相应的任务是标识如此命名的实体。这是一个不同的任务，一个更抽象的任务，在产生答案时涉及更明确的推理。在真正意义上，任务是检测“不存在”的东西。
+总的来说，ACE 程序的动机和解决的问题与之前的 MUC 程序相同。然而，ACE 程序以目标对象(即实体、关系和事件)来定义研究目标，而不是以文本中的词语来定义。例如，在 MUC 中定义的所谓“命名实体”任务就是识别(页面上的)实体名称的单词。而在 ACE 中，相应的任务是标识如此命名的实体。后一个任务更加抽象，在产生答案时涉及更明确的推理。从某种意义上说，AUC检测的实体“并不在那里”。
 While the ACE program is directed toward extraction of information from [[Sound|audio]] and [[image]] sources in addition to pure text, the research effort is restricted to information extraction from text. The actual [[transduction (machine learning)|transduction]] of audio and image data into text is not part of the ACE research effort, although the processing of [[Speech recognition | ASR]] and [[Optical character recognition | OCR]] output from such transducers is.
@@ 第24行： / 第24行： @@
 While the ACE program is directed toward extraction of information from audio and image sources in addition to pure text, the research effort is restricted to information extraction from text. The actual transduction of audio and image data into text is not part of the ACE research effort, although the processing of  ASR and  OCR output from such transducers is.
-虽然 ACE 项目的目标是从音频和图像资源中提取除纯文本以外的信息，但研究工作仅限于从文本中提取信息抽取。实际将音频和图像数据转换成文本并不是 ACE 研究工作的一部分，尽管这些转换器输出的 ASR 和 OCR 信号的处理是 ACE 研究工作的一部分。
+虽然 ACE 项目的目标是从音频和图像资源中提取除纯文本以外的信息，但研究工作仅限于从文本中提取信息抽取。实际将音频和图像数据转换成文本并不是 ACE 研究工作的一部分，不过这些转换器输出的 ASR 和 OCR 的输出是 ACE 研究工作的一部分。
 The effort involves:
@@ 第38行： / 第38行： @@
 这项工作包括:
 * 详细定义研究任务，
-* 收集和注释培训、开发和评估所需的数据，
+* 收集和注释数据集，包括训练集测试集等，
 * 通过评估工具和研究工作坊支持研究。
@@ 第52行： / 第52行： @@
 # events mentioned in the text, such as: interaction, movement, transfer, creation and destruction.
-给定一个自然语言的文本，ACE 的挑战是检测: 文本中提到的 # 实体，如: 个人、组织、地点、设施、武器、车辆和地理政治实体。# 实体之间的关系，例如: a 人是 b 公司的经理。关系类型包括: 角色、部分、位置、接近和社会。# 文本中提到的事件，比如: 互动，运动，转移，创造和毁灭。
+给定一个自然语言的文本，ACE 的挑战是检测: 文本中提到的
 The program relates to [[English language|English]], [[Arabic language|Arabic]] and [[Chinese language|Chinese]] texts.

@@ 第1行： / 第1行： @@
-此词条暂由彩云小译翻译，翻译字数共388，未经人工整理和审校，带来阅读不便，请见谅。
+此词条暂由彩云小译翻译，翻译字数共388，人工整理：林家驹。
 '''Automatic content extraction''' ('''ACE''') is a research program for developing advanced [[information extraction]] [[technologies]] convened by the [[National Institute of Standards and Technology|NIST]] from 1999 to 2008, succeeding [[Message Understanding Conference|MUC]] and preceding [https://www.nist.gov/tac/ Text Analysis Conference].
 自动内容提取(ACE)是由 NIST 在1999年至2008年间召开的一个研究项目，目的是开发先进的信息抽取/文本分析技术，后续还有 MUC 和之前的文本分析会议。
 ==Goals and efforts==
 In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations, and the events) rather than in terms of the words in the text. For example, the so-called "named entity" task, as defined in MUC, is to identify those words (on the page) that are names of entities. In ACE, on the other hand, the corresponding task is to identify the entity so named. This is a different task, one that is more abstract and that involves inference more explicitly in producing an answer. In a real sense, the task is to detect things that "aren't there".
@@ 第21行： / 第11行： @@
 While the ACE program is directed toward extraction of information from [[Sound|audio]] and [[image]] sources in addition to pure text, the research effort is restricted to information extraction from text. The actual [[transduction (machine learning)|transduction]] of audio and image data into text is not part of the ACE research effort, although the processing of [[Speech recognition | ASR]] and [[Optical character recognition | OCR]] output from such transducers is.
 虽然 ACE 项目的目标是从音频和图像资源中提取除纯文本以外的信息，但研究工作仅限于从文本中提取信息抽取。实际将音频和图像数据转换成文本并不是 ACE 研究工作的一部分，不过这些转换器输出的 ASR 和 OCR 的输出是 ACE 研究工作的一部分。
@@ 第29行： / 第17行： @@
 * defining the research tasks in detail,
 * collecting and annotating data needed for training, development, and evaluation,
-* supporting the research with evaluation tools and [[research workshop]]s.
+* supporting the research with evaluation tools and [[research workshop]]s
 这项工作包括:
@@ 第45行： / 第28行： @@
 # '''entities''' mentioned in the text, such as: persons, organizations, locations, facilities, weapons, vehicles, and geo-political entities.
 # '''relations''' between entities, such as: person A is the manager of company B. Relation types include: role, part, located, near, and social.
-# '''events''' mentioned in the text, such as: interaction, movement, transfer, creation and destruction.
+# '''events''' mentioned in the text, such as: interaction, movement, transfer, creation and destruction
 给定一个自然语言的文本，ACE 的挑战是检测: 文本中提到的
@@ 第61行： / 第39行： @@
 The program relates to [[English language|English]], [[Arabic language|Arabic]] and [[Chinese language|Chinese]] texts.
 该计划涉及英语，阿拉伯语和中文文本。
 The ACE corpus is one of the standard benchmarks for testing new information extraction [[algorithm]]s.
 ACE 语料库是测试新的信息抽取算法的标准基准之一。
@@ 第74行： / 第48行： @@
 ==References==
 * George Doddington@NIS T, Alexis Mitchell@LD C, Mark Przybocki@NIS T, Lance Ramshaw@BB N, Stephanie Strassel@LD C, Ralph Weischedel@BB N. [https://web.archive.org/web/20150126022215/http://www.citeulike.org/user/erelsegal-halevi/article/10003935 The automatic content extraction (ACE) program–tasks, data, and evaluation.] 2004
 ==External links==