Unsupervised corpus–wide claim detection

Abstract

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.

Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining

“…For the topic-dependent claim detection task, Levy et al (2017) showed that retrieving sentences with the word that followed by the concept representing the topic, yields candidates that are more likely to contain a claim for that topic than arbitrary sentences which contain the topic concept.…”

Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining

“…Given a controversial topic, several queries are applied, retrieving sentences which potentially contain claims that are relevant to the topic. Query results are then ranked by a neural-model trained to detect sentences containing claims (similarly to Levy et al (2017Levy et al ( , 2018 ). Top-ranked sentences are passed to a boundary detection component, responsible for finding the exact span of each claim within each sentence (Levy et al, 2014).…”

Lexicon Guided Attentive Neural Network Model for Argument Mining

“…The claim lexicon is a lexicon containing words with argumentative characteristics. Levy et al (2017) use the appearance of the term "that" as a weak signal of sentences containing argumentative components. After collecting nearly 1.86M sentences, they compute the prior probability of the term "that" P (that) occurring in a sentence, and the probabilities P (that|w i ), where P (that|w i ) denotes the probability of a sentence having the term "that" and the word w i is in the suffix after the main concept (i.e.…”