Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration. Previous works on mining argumentative content have assumed that a set of relevant documents is given in advance. Here, we present a first corpuswide claim detection framework, that can be directly applied to massive corpora. Using simple and intuitive empirical observations, we derive a claim sentence query by which we are able to directly retrieve sentences in which the prior probability to include topic-relevant claims is greatly enhanced. Next, we employ simple heuristics to rank the sentences, leading to an unsupervised corpus-wide claim detection system, with precision that outperforms previously reported results on the task of claim detection given relevant documents and labeled data.
The only work we are aware of that tackles corpus wide claim detection, is the work by REF .
Unsupervised corpus–wide claim detection
ArgMining@EMNLP
Computer Science