# Understanding Enthymemes in Argument Maps: Bridging Argument Mining and Logic-based Argumentation

arXiv: https://arxiv.org/abs/2408.08648 · HTML: https://mlsanity.com/paper/2408.08648/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 76 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://mlsanity.com/paper/2305.12295/) (2023)
- [LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://mlsanity.com/paper/2310.15164/) (2023)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Every Bite Is an Experience: Key Point Analysis of Business Reviews](https://mlsanity.com/paper/2106.06758/) (2021)
- [Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection](https://mlsanity.com/paper/2405.02318/) (2024)
- [Exploring Neural Models for Parsing Natural Language into First-Order Logic](https://mlsanity.com/paper/2002.06544/) (2020)
- [A Comparative Study of Ranking-based Semantics for Abstract Argumentation](https://mlsanity.com/paper/1602.01059/) (2016)
- [Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments](https://mlsanity.com/paper/1802.07489/) (2018)
- [Inducing and Using Alignments for Transition-based AMR Parsing](https://mlsanity.com/paper/2205.01464/) (2022)
- [Argument Ranking with Categoriser Function](https://mlsanity.com/paper/1406.3877/) (2014)
- [Attacker and Defender Counting Approach for Abstract Argumentation](https://mlsanity.com/paper/1506.04272/) (2015)
- [Neuro-symbolic Commonsense Social Reasoning](https://mlsanity.com/paper/2303.08264/) (2023)
- [NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes](https://mlsanity.com/paper/2402.14458/) (2024)
- [Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy](https://mlsanity.com/paper/2205.11966/) (2022)
- [AMR Parsing with Instruction Fine-tuned Pre-trained Language Models](https://mlsanity.com/paper/2304.12272/) (2023)
- [Identification of Entailment and Contradiction Relations for Natural Language Argumentation: A Neurosymbolic Approach](https://mlsanity.com/paper/2405.01259/) (2024)
- [Using Enthymemes to Fill the Gap between Logical Argumentation and Revision of Abstract Argumentation Frameworks](https://mlsanity.com/paper/1603.08789/) (2016)
- [Non-monotonic Reasoning in Deductive Argumentation](https://mlsanity.com/paper/1809.00858/) (2018)
- [Some Options for Instantiation of Bipolar Argument Graphs with Deductive Arguments](https://mlsanity.com/paper/2308.04372/) (2023)

## Built on by (most cited first, 2 of 2)

- [An Axiomatic Study of the Evaluation of Enthymeme Decoding in Weighted Structured Argumentation](https://mlsanity.com/paper/2411.04555/) (2024)
- [Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study](https://mlsanity.com/paper/2604.12534/) (2026)

## Abstract (arXiv)

Argument mining is natural language processing technology aimed at identifying arguments in text. Furthermore, the approach is being developed to identify the premises and claims of those arguments, and to identify the relationships between arguments including support and attack relationships. In this paper, we assume that an argument map contains the premises and claims of arguments, and support and attack relationships between them, that have been identified by argument mining. So from a piece of text, we assume an argument map is obtained automatically by natural language processing. However, to understand and to automatically analyse that argument map, it would be desirable to instantiate that argument map with logical arguments. Once we have the logical representation of the arguments in an argument map, we can use automated reasoning to analyze the argumentation (e.g. check consistency of premises, check validity of claims, and check the labelling on each arc corresponds with thw logical arguments). We address this need by using classical logic for representing the explicit information in the text, and using default logic for representing the implicit information in the text. In order to investigate our proposal, we consider some specific options for instantiation.