Unsupervised corpus--wide claim detection

WS 2017  ·  Ran Levy, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, Noam Slonim ·

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 corpus{--} wide 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. read more

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here