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 171–180 of 284 papers

TitleStatusHype
End-to-End Argument Mining as Biaffine Dependency Parsing—0
End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture—0
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis—0
ESCRITO - An NLP-Enhanced Educational Scoring Toolkit—0
Evidence Type Classification in Randomized Controlled Trials—0
Evidence Types, Credibility Factors, and Patterns or Soft Rules for Weighing Conflicting Evidence: Argument Mining in the Context of Legal Rules Governing Evidence Assessment—0
Expert Stance Graphs for Computational Argumentation—0
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources—0
Exploring new Approaches for Information Retrieval through Natural Language Processing—0
Extracting Argument and Domain Words for Identifying Argument Components in Texts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified