开放信息提取技术综述

Sakhawat Ali, Hamdy M. Mousa, M. Hussien
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引用次数: 6

摘要

如今,大量的数据每时每刻都在流动。这些数据中大约有20%到30%是文本。这些数据总是以半结构化文本的形式组织,不能直接使用。为了利用如此大量的文本数据,需要以一种快速和可扩展的方式检测、提取和构建通过这些数据传递的信息。这可以使用信息提取技术来执行。然而,信息提取任务是自然语言处理的主要挑战之一,它在大规模数据上的实现存在局限性。开放信息抽取(OIE)是一种以无监督方式进行信息抽取的开放领域和关系无关的范式。这种技术可以带来高速和可扩展的性能。回顾以往的研究建议,OIE在不同语言之间进行了实验,如英语、葡萄牙语、西班牙语、越南语、汉语和德语。本文综述了OIE技术,比较了它们在不同语言下的性能,并将这些结果与语言复杂程度相结合,揭示了合适的模型与语言复杂程度之间的关系。如今,大量的数据每时每刻都在流动。这些数据中大约有20%到30%是文本。这些数据总是以半结构化文本的形式组织,不能直接使用。为了利用如此大量的文本数据,需要以一种快速和可扩展的方式检测、提取和构建通过这些数据传递的信息。这可以使用信息提取技术来执行。然而,信息提取任务是自然语言处理的主要挑战之一,它在大规模数据上的实现存在局限性。开放信息抽取(OIE)是一种以无监督方式进行信息抽取的开放领域和关系无关的范式。这种技术可以带来高速和可扩展的性能。回顾以往的研究建议,OIE在不同语言之间进行了实验,如英语、葡萄牙语、西班牙语、越南语、汉语和德语。本文综述了OIE技术,比较了它们在不同语言下的性能,并将这些结果与语言复杂程度相结合,揭示了合适的模型与语言复杂程度之间的关系。关键词:公开信息提取;自然语言处理
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Open Information Extraction Techniques
Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level. Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processing
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