# TACAM: Topic And Context Aware Argument Mining

## TL;DR

This paper introduces TACAM, a novel argument mining approach that incorporates topic and external context information, significantly improving the accuracy of identifying arguments within large text corpora.

## Contribution

It proposes models that integrate topic context and external knowledge sources for argument classification, advancing argument mining beyond traditional topic-independent methods.

## Key findings

- Topic-aware models outperform baseline approaches.
- External knowledge integration boosts argument classification accuracy.
- Contextual models significantly improve argument detection performance.

## Abstract

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task.