Approaches to Text Mining Arguments from Legal Cases

Wyner, Adam; Mochales-Palau, Raquel; Moens, Marie‐Francine; Milward, David

doi:10.1007/978-3-642-12837-0_4

Cited by 93 publications

(63 citation statements)

References 34 publications

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“…Whilst the methodology developed does not aim to fully reconstruct argumentation structure, the information obtained during the study can be used as a part of a wider application. Wyner et al (2010) conducted a study aimed at identification of argumentation parts with the use of context-free grammars. Similar to Jackson et al (2003) the study reports the following difficulties with identifying argumentation structures in legal texts: ''(a) the detection of intermediate conclusions, especially the ones without rhetorical markers, as more than 20% of the conclusions are classified as premises of a higher layer conclusion; (b) the ambiguity between argument structures.''…”

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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“…(Boltuzic andŠnajder, 2014) use Textual Entailment to identify support relations between posts in discussion fora. Other application areas for Argumentation Mining have been the biomedical (Faiz and Mercer, 2014;Green, 2014;Houngbo and Mercer, 2014) and legal domains, where the well-structured nature of legal text and the development of corpora such as the ECHR corpus (Mochales and Moens, 2008) have sparked development in this area Wyner et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

“…To date, researchers have investigated methods for argumentation mining of legal documents (Mochales and Moens 2011;Bach et al 2013;Ashley and Walker 2013;Wyner et al 2010), on-line debates (Cabrio and Villata 2012), product reviews (Villalba and Saint-Dizier 2012;, user comments on proposed regulations , newspaper articles and court cases . A related older strand of research (that uses the term 'argumentative structure' in a related but different sense than ours) has investigated automatically classifying the sentences of a scientific article's abstract or full text in terms of their contribution of new knowledge to a field (e.g., Liakata et al 2012, Teufel 2010, Mizuta et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…The primary domain for argumentation mining has been professionally written text, such as parliamentary records, legal documents and news articles, which contain well-formed arguments consisting of explicitly stated premises and conclusions Wyner et al, 2010;Ashley and Walker, 2013). In contrast, online user comments are often comprised of implicit arguments, which are conclusions with no explicitly stated premises 1 .…”

Section: Introductionmentioning

confidence: 99%

“…Typically, documents in which such structures are abundant, such as from the legal domain (Mochales Palau and Moens, 2011;Bach et al, 2013;Ashley and Walker, 2013;Wyner et al, 2010), are analyzed, and compound argumentative structures, or argumentation schemes, are sought (Walton, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Proceedings of the 2nd Workshop on Argumentation Mining

Cardie¹,

Green²,

Gurevych³

et al. 2015

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ii BackgroundThe goal of this workshop is to provide a follow-on forum to last year's very successful Argumentation Mining workshop at ACL, the first research forum devoted to argumentation mining in all domains of discourse.Argumentation mining is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within a document, e.g., the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argumentcounterargument relationships between pairs of arguments in the document. To date, researchers have investigated methods for argumentation mining of legal documents (Mochales and Moens 2011;Bach et al. 2013;Ashley and Walker 2013;Wyner et al. 2010), on-line debates (Cabrio and Villata 2012), product reviews (Villalba and Saint-Dizier 2012;, user comments on proposed regulations (Park and Cardie 2014), newspaper articles and court cases . A related older strand of research (that uses the term 'argumentative structure' in a related but different sense than ours) has investigated automatically classifying the sentences of a scientific article's abstract or full text in terms of their contribution of new knowledge to a field (e.g., Liakata et al. 2012, Teufel 2010, Mizuta et al. 2005). In addition, argumentation mining has ties to sentiment analysis (e.g., Somasundaran and Wiebe 2010). To date there are few corpora with annotations for argumentation mining research ) although corpora with annotations for argument sub-components have recently become available (e.g., Park and Cardie 2014).Proposed applications of argumentation mining include improving information retrieval and information extraction as well as end-user visualization and summarization of arguments. Textual sources of interest include not only the formal writing of legal text, but also a variety of informal genres such as microtext, spoken meeting transcripts, product reviews and user comments. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students' command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction (see e.g., Ong, Litman and Brusilovsky 2014). This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs.As one might expect, success in argumentation mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require the creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.We are looking forward to a full day workshop to exchange i...

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The representation of argumentation in scientific papers: A comparative analysis of two research areas

Wang

Song

Zhou

et al. 2021

Asso for Info Science & Tech

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Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure.Scientific papers are the most essential manifestations of evolving scientific knowledge, and argumentation is an important avenue through which research results are communicated (Walton & Zhang, 2013). Scientific discovery challenges existing theoretical analyses and empirical studies in a process of continuous argumentation; thus, argumentation forms the foundation of scientific papers, and articles demonstrating a rigorous argumentation process are essential for scientific development. Meanwhile, the "argumentation units" (along with the argumentation structure within a scientific paper) are a special implicit knowledge entity that represents the attribute of knowledge.Argumentation plays a critical role in identifying relevant arguments based on their content; thus, it seems reasonable to focus on identifying characteristic patterns of argumentation and the ways in which they are typically developed (Song et al., 2014). Revealing, extracting, and analyzing the argumentation structure of scientific papers plays an important role in interpreting the general rules of their content composition and benefits knowledge discovery, representation, retrieval, and ultimately access. Therefore, it is necessary to understand how argumentation is represented within scientific papers and to identify argument structures when they are extended to whole text segments (Walton, 2013;Walton et al., 2008).Recent research has placed particular emphasis on the definition, description, and representation of scientific content from the semantic and pragmatic perspectives. Several ontologies, such as the document element

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Approaches to Text Mining Arguments from Legal Cases

Cited by 93 publications

References 34 publications

Recognizing cited facts and principles in legal judgements

Recognizing cited facts and principles in legal judgements

Proceedings of the 2nd Workshop on Argumentation Mining

The representation of argumentation in scientific papers: A comparative analysis of two research areas

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