Argument mining, or argumentation mining, is a research area within the
natural-language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from
natural language
A natural language or ordinary language is a language that occurs naturally in a human community by a process of use, repetition, and change. It can take different forms, typically either a spoken language or a sign language. Natural languages ...
text with the aid of computer programs.
[ Such 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.][ The Argument Mining workshop series is the main research forum for argument mining related research.][
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Applications
Argument mining has been applied in many different genres including the qualitative assessment of social media
Social media are interactive technologies that facilitate the Content creation, creation, information exchange, sharing and news aggregator, aggregation of Content (media), content (such as ideas, interests, and other forms of expression) amongs ...
content (e.g. Twitter, Facebook), where it provides a powerful tool for policy-makers and researchers in social and political sciences.[ Other domains include legal documents, product reviews, scientific articles, online debates, newspaper articles and dialogical domains. ]Transfer learning
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recogniz ...
approaches have been successfully used to combine the different domains into a domain agnostic argumentation model.
Argument mining has been used to provide students individual writing support by accessing and visualizing the argumentation discourse in their texts. The application of argument mining in a user-centered learning tool helped students to improve their argumentation skills significantly compared to traditional argumentation learning applications.
Challenges
Given the wide variety of text genres and the different research perspectives and approaches, it has been difficult to reach a common and objective evaluation scheme.[ Many annotated data sets have been proposed, with some gaining popularity, but a consensual data set is yet to be found.
Annotating argumentative structures is a highly demanding task. There have been successful attempts to delegate such annotation tasks to the crowd but the process still requires a lot of effort and carries significant cost. Initial attempts to bypass this hurdle were made using the weak supervision approach.][
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See also
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References
Argument technology
Computational linguistics
Data mining
Information retrieval
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