''You should probably read this'': Hedge Detection in Text

TL;DR

This paper presents a joint model combining words and part-of-speech tags to improve hedge detection in text, achieving top performance on the CoNLL-2010 Wikipedia corpus, which is important for understanding certainty in critical domains.

Contribution

The paper introduces a novel joint model that leverages both words and part-of-speech tags for hedge detection, setting a new state-of-the-art on the CoNLL-2010 dataset.

Findings

Achieved new top score on CoNLL-2010 Wikipedia corpus

Joint model improves hedge detection accuracy

Demonstrates importance of linguistic features in certainty detection

Abstract

Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus.