What is Argument Mining?

What is Argument Mining?

Argument mining, also known as argumentation mining, is a research area within the field of natural language processing (NLP). Its primary goal is the automatic extraction and identification of argumentative structures from natural language text. These argumentative structures include the premise, conclusions, the argument scheme, and the relationship between the main and subsidiary argument, or the main and counter-argument within discourse.

Understanding the argumentative structure of a text can provide valuable insights into not only what positions people hold, but also why they hold the opinions they do. This can be particularly useful in diverse domains such as financial market prediction and public policy.

Argument mining is a challenging task due to the complexity of human language and reasoning. It involves identifying and extracting the structure of inference and reasoning expressed as arguments in a text. The term "argument" in this context refers to a set of phrases or sentences that function as "premises" along with another set of phrases or sentences that function as "conclusions" or "claims".

Applications of argument mining span various genres, including social media platforms like Twitter and Facebook, legal documents, product reviews, scientific articles, online debates, and newspaper articles. It provides a powerful tool for policy-makers and researchers in social and political sciences, among other fields.

Despite its potential, argument mining faces several challenges. For instance, when explicit discourse markers are present in language utterances, the argumentation can be interpreted by a machine with an acceptable degree of accuracy. However, in many real settings, the mining task is difficult due to the absence of such markers. Nonetheless, advancements in machine learning and NLP are continually improving the capabilities of argument mining systems.

What are some techniques used in argument mining?

Argument mining is a field within natural language processing that aims to automatically identify and extract argumentative structures from text. These structures include premises, conclusions, argument schemes, and relationships between main and subsidiary arguments or main and counter-arguments. Here are some techniques used in argument mining:

1. Machine Learning and Natural Language Processing (NLP) Techniques — These are commonly used in argument mining systems. They help in identifying and categorizing argumentative components in a text, such as claims, reasons, and their relations.

2. Neural-Symbolic Argumentation Mining — This approach combines deep learning and reasoning to perform joint detection of argument components and relations through a single learning process.

3. Probabilistic Logic Modeling — This technique is used to model and analyze argumentation. The formalization of arguments is usually addressed at two levels, and this method helps in understanding the structure of arguments.

4. Structured SVMs and RNNs — Structured Support Vector Machines (SVMs) and Recurrent Neural Networks (RNNs) are used to propose novel factor graph models for argument mining.

5. BiLSTM-Attention Model — This model is used for argument mining in English as a Foreign Language (EFL) writing. It involves two subtasks: Argument Component Identification (ACI), which identifies the location and components of an argument, and Argument Relation Identification (ARI), which identifies the relationships between argument components.

6. Shallow Techniques — These techniques are used to extract arguments from text. They are considered "shallow" because they don't delve deep into the semantic or logical structure of the argument but focus on surface-level features.