Argumentation Mining System for Corpus-based Discourse Analysis based on Structured Arguments

Abstract

An Argumentation mining system can analyze a large volume of text data through a variety of sources. Nowadays it is highly useful in the areas of business, economics, and finance with digital marketing being the most promising field along with social media. It is the study of corpus-based discourse analysis that involves the automatic identification of argumentative structure in text. Initially, AM talks about extracting structured arguments from natural text, often unstructured or noisy text. Theoretical approaches of AM and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable of adjusting to argumentation conditions that exist in social media. In this scenario it is a very challenging argumentation scheme able to identify the distinct sub-task and capture the needs of social media text, revealing the need for adopting a more flexible and extensible framework. Corpus-based Machine Learning of linguistic annotations has enabled researchers to identify repetitive linguistic patterns of language use and to uncover hidden meaning in all areas of Natural Language Processing.