Automatic detection of arguments in legal texts

Moens, Marie‐Francine; Boiy, Erik; Palau, Raquel Mochales; Reed, Chris

doi:10.1145/1276318.1276362

Cited by 184 publications

(137 citation statements)

References 21 publications

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“…The methodology of applying statistical tools over annotated corpus was employed by Moens et al (2007) to automatically detect sentences that are a part of the legal argument. The study achieved 68% accuracy for legal texts.…”

Section: Argument Extractionmentioning

confidence: 99%

Recognizing cited facts and principles in legal judgements

2017

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In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, j ¼ 0:65 and j ¼ 0:95 for inter-and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall j of 0.72 with the human-annotated gold standard.

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Section: Argument Extractionmentioning

confidence: 99%

Recognizing cited facts and principles in legal judgements

2017

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“…Just a few years ago, the prospect of automatically extracting the structure of reasoning from natural language text was firmly beyond the state of the art: only very preliminary work was being carried out at a small number of research groups such as Leuven, Dundee, and Toronto (Feng and Hirst 2011;Moens et al 2007). Now, there are at least twenty research groups across the US and the EU, in which work on the problem has begun in earnest, with three workshops, including one at the major computational linguistics conference, ACL, during the summer of 2014.…”

Section: Argument Miningmentioning

confidence: 99%

The Argument Web: an Online Ecosystem of Tools, Systems and Services for Argumentation

et al. 2017

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The Argument Web is maturing as both a platform built upon a synthesis of many contemporary theories of argumentation in philosophy and also as an ecosystem in which various applications and application components are contributed by different research groups around the world. It already hosts the largest publicly accessible corpora of argumentation and has the largest number of interoperable and cross compatible tools for the analysis, navigation and evaluation of arguments across a broad range of domains, languages and activity types. Such interoperability is key in allowing innovative combinations of tool and data reuse that can further catalyse the development of the field of computational argumentation. The aim of this paper is to summarise the key foundations, the recent advances and the goals of the Argument Web, with a particular focus on demonstrating the relevance to, and roots in, philosophical argumentation theory.

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“…Humans are very keen to convince others of their opinion and bring arguments to a discussion to support their claims. Finding arguments in an automated way in human discourse was early on discovered as a desirable characteristic of intelligent machines or agents [48], often referred to as argumentation mining or argument mining [10,46]. Throughout this paper we use the term "argumentation" as discourse phrases or sentences or larger discourse units that have an argumentative function in this discourse.…”

Section: Introductionmentioning

confidence: 99%

Argumentation mining: How can a machine acquire common sense and world knowledge?

Moens

2018

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Abstract. 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.

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Automatic detection of arguments in legal texts

Cited by 184 publications

References 21 publications

Recognizing cited facts and principles in legal judgements

Recognizing cited facts and principles in legal judgements

The Argument Web: an Online Ecosystem of Tools, Systems and Services for Argumentation

Argumentation mining: How can a machine acquire common sense and world knowledge?

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