SOTAVerified

Argument Mining

Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.

Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Papers

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Showing 211–220 of 284 papers

TitleStatusHype
Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions.—0
Quantifying Qualitative Data for Understanding Controversial Issues—0
A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments—0
Visualizing the Flow of Discourse with a Concept OntologyCode0
Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks—0
Lightly-Supervised Modeling of Argument Persuasiveness—0
Argument Relation Classification Using a Joint Inference Model—0
Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization—0
Building an Argument Search Engine for the Web—0
Manual Identification of Arguments with Implicit Conclusions Using Semantic Rules for Argument Mining—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified