Enriching Argumentative Texts with Implicit Knowledge

Becker, Maria; Staniek, Michael; Năstase, Vivi; Frank, Anette

doi:10.1007/978-3-319-59569-6_9

Cited by 10 publications

(16 citation statements)

References 7 publications

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“…In this argument, the argumentative relation classification would return the relations shown in Fig. 2 4, a large part of enthymemes cannot be reconstructed by using just reasoning, as they also require background knowledge. Sect.…”

Section: Overview Of Argument Explicitationmentioning

confidence: 99%

“…While such information can be easily filled in by the hearer, a computational system typically does not possess the knowledge that is needed to reconstruct the implied information. Especially in argumentative texts it is very common that premises are implied and omitted [4,22,43]. These arguments are called enthymemes.…”

Section: Implicit Knowledge In Argumentative Textsmentioning

confidence: 99%

“…In a recent annotation project [2,4] (1-b) even if billions of Euros have already been invested in the existing airport project. (1-c) BER is an airport.…”

Section: Learning From Human-generated Datamentioning

confidence: 99%

“…Annotators were asked to add the information that makes connections between given unit pairs explicit, using short and simple sentences. We designed an annotation process that involves several steps to promote annotator agreement and that allows us to monitor its evolution using textual similarity measures [4]. Fig.…”

Section: Learning From Human-generated Datamentioning

confidence: 99%

“…To learn more about the nature and characteristics of the inserted information and the overt argumentative texts, we further annotated the data with two specific semantic information types which we found to be characteristic for argumentative texts in two studies [3,4]: (I) Semantic clause types, from which we adopted the most frequent types in [17] (states, events, generic sentences, and generalizing sentences), and (II) ConceptNet commonsense knowledge relations [49,50], which comprise an inventory of 37 relations, some of which are commonly used in other resources like WordNet (e.g., IsA, PartOf) while most others are targeted for capturing commonsense knowledge and as such are particular to ConceptNet (e.g., HasPrerequisite, MotivatedByGoal). Two example annotations from our dataset are given in Fig.…”

Section: Learning From Human-generated Datamentioning

confidence: 99%

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Explaining Arguments with Background Knowledge

et al. 2020

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Most information we consume as a society is obtained over the Web. News-often from questionable sources-are spread online, as are election campaigns; calls for (collective) action spread with unforeseen speed and intensity. All such actions have argumentation at their core, and the conveyed content is often strategically selected or rhetorically framed. The responsibility of critical analysis of arguments is thus tacitly transferred to the content consumer who is often not prepared for the task, nor aware of the responsibility. The ExpLAIN project aims at making the structure and reasoning of arguments explicit-not only for humans, but for Robust Argumentation Machines that are endowed with language understanding capacity. Our vision is a system that is able to deeply analyze argumentative text: that identifies arguments and counter-arguments, and reveals their internal structure, conveyed content and reasoning. A particular challenge for such a system is to uncover implicit knowledge which many arguments rely on. This requires human background knowledge and reasoning capacity, in order to explicate the complete reasoning of an argument. This article presents ongoing research of the ExpLAIN project that aims to make the vision of such a system a tangible aim. We introduce the problems and challenges we need to address, and present the progress we achieved until now by applying advanced natural language and knowledge processing methods. Our approach puts particular focus on leveraging available sources of structured and unstructured background knowledge, the automatic extension of such knowledge, the uncovering of implicit content, and reasoning techniques suitable for informal, everyday argumentation.

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Section: Overview Of Argument Explicitationmentioning

confidence: 99%

Section: Implicit Knowledge In Argumentative Textsmentioning

confidence: 99%

“…In a recent annotation project [2,4] (1-b) even if billions of Euros have already been invested in the existing airport project. (1-c) BER is an airport.…”

Section: Learning From Human-generated Datamentioning

confidence: 99%

Section: Learning From Human-generated Datamentioning

confidence: 99%