In this paper, we compose a new task for deep argumentative structure analysis that goes beyond shallow discourse structure analysis. The idea is that argumentative relations can reasonably be represented with a small set of predefined patterns. For example, using value judgment and bipolar causality, we can explain a support relation between two argumentative segments as follows: Segment 1 states that something is good, and Segment 2 states that it is good because it promotes something good when it happens. We are motivated by the following questions: (i) how do we formulate the task?, (ii) can a reasonable pattern set be created?, and (iii) do the patterns work? To examine the task feasibility, we conduct a three-stage, detailed annotation study using 357 argumentative relations from the argumentative microtext corpus, a small, but highly reliable corpus. We report the coverage of explanations captured by our patterns on a test set composed of 270 relations. Our coverage result of 74.6 indicates that argumentative relations can reasonably be explained by our small pattern set. Our agreement result of 85.9 shows that a reasonable inter-annotator agreement can be achieved. To assist with future work in computational argumentation, the annotated corpus is made publicly available.
Conventionally, discourse structure analysis has been studied in the context of discourse relation identification. Earlier work includes discourse theories such as rhetorical structure theory which aim at creating coherent, tree-like structures for describing texts, where text units are typically adjacent @cite_15 . Other theories such as cross-document structure theory focus on the identification of discourse relations spanned across multiple documents @cite_17 . The Penn Discourse TreeBank, the largest manually annotated corpus for discourse relations, targeted both implicit and explicit relation detection for either adjacent sentences or clauses @cite_4 . In the field of Argumentation mining, previous work has proposed several kinds of tasks such as structure identification task (e.g., support-attack relation detection) @cite_5 @cite_13 . In addition, a wide variety of corpora have been created in several domains including scientific articles, essays, and online discussions @cite_18 @cite_9 . These studies aim to capture the shallow structures of arguments and do not try to explain a writer's reasoning.
@cite_18 @cite_4 @cite_9 @cite_5 @cite_15 @cite_13 @cite_17
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In this paper, we argue that an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties. There is no one-size-fits-all argumentation theory to be applied to realistic data on the Web. In two annotation studies, we experiment with 80 German newspaper editorials from the Web and about one thousand English documents from forums, comments, and blogs. Our example topics are taken from the educational domain. To formalize the problem of annotating arguments, in the first case, we apply a Claim-Premise scheme, and in the second case, we modify Toulmin's scheme. We find that the choice of the argument components to be annotated strongly depends on the register, the length of the document, and inherently on the literary devices and structures used for expressing argumentation. We hope that these findings will facilitate the creation of reliably annotated argumentation corpora for a wide range of tasks and corpus types and will help to bridge the gap between argumentation theories and actual application needs. We present the second version of the Penn Discourse Treebank, PDTB-2.0, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus. We describe all aspects of the annotation, including (a) the argument structure of discourse relations, (b) the sense annotation of the relations, and (c) the attribution of discourse relations and each of their arguments. We list the differences between PDTB-1.0 and PDTB-2.0. We present representative statistics for several aspects of the annotation in the corpus. In this paper, we present a novel approach to model arguments, their components and relations in persuasive essays in English. We propose an annotation scheme that includes the annotation of claims and premises as well as support and attack relations for capturing the structure of argumentative discourse. We further conduct a manual annotation study with three annotators on 90 persuasive essays. The obtained inter-rater agreement of αU =0 .72 for argument components and α =0 .81 for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to substantial agreement. The final corpus and the annotation guidelines are freely available to encourage future research in argument recognition. Many argumentation tools have been proposed nowadays to support the users in on-line social discussions. However, the main drawback of these tools is that they do not cope with the automatic generation of the arguments from the natural language discussions of the users. In this paper, we propose to use a technique from computational linguistics, namely textual entailment, to generate in an automatic way the abstract arguments from the dialogues. The abstract arguments as well as their relationships are then structured in an argumentation graph to evaluate the dialogue as a whole. The success criteria of the proposed approach is that it is able to represent the dynamics of the dialogues among users allowing to find the use of argumentation natural enough to be really adopted. Rhetorical Structure Theory (RST) is a theory of text structure that is being extended to serve as a theoretical basis for computational text planning. Text structures in RST are hierarchic, built on small patterns called schemas. The schemas which compose the structural hierarchy of a text describe the functions of the parts rather than their form characteristics. Relations between text parts, comparable to conjunctive relations, are a prominent part of RST’s definitional machinery. Recent work has put RST onto a new definitional basis. This paper details the current status of descriptive RST, along with efforts to create a constructive version for use as a basis for programming a text planner. We introduce CST (cross-document structure theory), a paradigm for multi-document analysis. CST takes into account the rhetorical structure of clusters of related textual documents. We present a taxonomy of cross-document relationships. We argue that CST can be the basis for multi-document summarization guided by user preferences for summary length, information provenance, cross-source agreement, and chronological ordering of facts.