30 papers with code • 0 benchmarks • 2 datasets
Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
Argument mining has become a popular research area in NLP.
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.
We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset.
Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role.
We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims.