Identifying the main claims occurring across texts is important for large-scale argumentation mining from social media. However, the claims that users make are often unclear and build on implicit knowledge, effectively introducing a gap between the claims. In this work, we study the problem of matching user claims to predefined main claims, using implicit premises to fill the gap. We build a dataset with implicit premises and analyze how human annotators fill the gaps. We then experiment with computational claim matching models that utilize these premises. We show that using manually-compiled premises improves similarity-based claim matching and that premises generalize to unseen user claims.
In their attempt to reconstruct implicit knowledge, REF find that the claims that users make in online debate platforms often build on implicit knowledge.
Fill the Gap! Analyzing Implicit Premises between Claims from Online Debates
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)
Computer Science