Summary of Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation, by Jonathan Ben-naim and Victor David and Anthony Hunter
Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation
by Jonathan Ben-Naim, Victor David, Anthony Hunter
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to analyzing argument maps generated from natural language processing (NLP) techniques. Specifically, it focuses on identifying arguments, premises, and claims, as well as relationships between them, such as support and attack. The authors assume that an argument map can be obtained automatically from text using NLP methods. However, to analyze the argument map effectively, they argue that it needs to be instantiated with logical representations of the arguments. This allows for automated reasoning techniques, like consistency checking and validity evaluation, to be applied. To achieve this, the paper combines classical logic for explicit information and default logic for implicit information. The authors explore specific instantiation options to test their proposal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to understand and analyze conversations. It’s like trying to figure out what people are arguing about and why they’re saying it. They want to use special computer programs to take a piece of text, like an article or a speech, and turn it into a map that shows the different points being made and how they relate to each other. To do this, they need to translate the text into a format that computers can understand, which is called logical representation. This will let them use special computer programs to check if what’s being said makes sense, or if someone is trying to trick others by using false information. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
