Implicit Knowledge in Argumentative Texts: An Annotated Corpus

TL;DR

This paper introduces a richly annotated corpus of argumentative texts highlighting implicit and implied information, aiming to improve automated argument analysis by understanding and reconstructing omitted knowledge.

Contribution

It presents a new high-quality annotated dataset of implicit information in argumentative texts, including semantic and commonsense annotations, with detailed analysis of its properties.

Findings

Revealed patterns in annotation categories and dataset properties

Identified correlations between argument structure and implicit knowledge

Provided insights to guide automatic implicit information extraction

Abstract

When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling such knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully de-signed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically.