Argumentation mining: How can a machine acquire common sense and world knowledge?
Argumentation mining: How can a machine acquire common sense and world knowledge?
复制标题
论证挖掘：机器如何获取常识和世界知识？
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Marie
中科院分区：
文献类型：
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作者：
Marie
。论证挖掘是机器对人类语言理解的高级形式。对于一台机器来说，这是一项具有挑战性的任务。当语言话语中存在明显的话语标记语(fi)时，机器能够以可接受的准确度解释论证。然而，在许多现实环境中，由于缺乏或模糊的话语标记语，挖掘任务是困难的，而且正确识别论证、其构成要素及其关系所需的大量知识并没有明确地出现在文本中，而是构成了人类在解释语言时所拥有的背景知识。在本文1中，我们重点介绍机器如何自动获取所需的常识和世界知识。由于在这方面的研究很少，本文提出的许多想法都是试探性的，但已经开始研究。我们概述了人类语言理解的最新方法，这些方法将语言映射到便于其他任务的形式化知识表示(例如，用于可视化论证的表示或易于在决策或论证支持系统中共享的表示)。大多数当前的系统都是针对手动标注的文本进行训练的。然后我们深入到新的fi表征学习领域，这是当今计算语言学非常研究的领域。这个fi领域研究了将语言表示为统计概念或向量的方法，从而允许直接的合成性方法。这些方法通常使用深度学习及其底层神经网络技术来以无监督的方式从大量文本集合中学习概念(即，不需要手动标注)。我们展示了这些方法如何帮助论证挖掘过程，但也表明这些方法需要进一步的研究来自动获取必要的背景知识和更具体的fi常识和世界知识。我们提出了一些通过利用文本和视觉数据来改善常识和世界知识学习的方法，并触及了如何将所学知识整合到论证挖掘过程中。
. Argumentation mining is an advanced form of human language understanding by the machine. This is a challenging task for a machine. When sufficient explicit discourse markers are present in the language utterances, the argumentation can be interpreted by the machine with an acceptable degree of accuracy. However, in many real settings, the mining task is difficult due to the lack or ambiguity of the discourse markers, and the fact that a substantial amount of knowledge needed for the correct recognition of the argumentation, its composing elements and their relationships is not explicitly present in the text, but makes up the background knowledge that humans possess when interpreting language. In this article 1 we focus on how the machine can automatically acquire the needed common sense and world knowledge. As very few research has been done in this respect, many of the ideas proposed in this article are tentative, but start being researched. We give an overview of the latest methods for human language understanding that map language to a formal knowledge representation that facilitates other tasks (for instance, a representation that is used to visualize the argumentation or that is easily shared in a decision or argumentation support system). Most current systems are trained on texts that are manually annotated. Then we go deeper into the new field of representation learning that nowadays is very much studied in computational linguistics. This field investigates methods for representing language as statistical concepts or as vectors, allowing straightforward methods of compositionality. The methods often use deep learning and its underlying neural network technologies to learn concepts from large text collections in an unsupervised way (i.e., without the need for manual annotations). We show how these methods can help the argumentation mining process, but also demonstrate that these methods need further research to automatically acquire the necessary background knowledge and more specifically common sense and world knowledge. We propose a number of ways to improve the learning of common sense and world knowledge by exploiting textual and visual data, and touch upon how we can integrate the learned knowledge in the argumentation mining process.
影响因子：
9.3
作者：
Edward Grefenstette;M. Sadrzadeh
通讯作者：
Edward Grefenstette;M. Sadrzadeh
DOI：
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发表时间：
2013
期刊：
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影响因子：
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作者：
Budzynska K
通讯作者：
Budzynska K
建模语义期望：使用脚本知识进行指示预测
DOI：
10.1162/tacl_a_00044
发表时间：
2017
影响因子：
10.9
作者：
A. Modi;I. Titov;V. Demberg;A. Sayeed;M. Pinkal
通讯作者：
M. Pinkal