Argumentation mining is considered as a key technology for future search engines and automated decision making. In such applications, argumentative text segments have to be mined from large and diverse document collections. However, most existing argumentation mining approaches tackle the classification of argumentativeness only for a few manually annotated documents from narrow domains and registers. This limits their practical applicability. We hence propose a distant supervision approach that acquires argumentative text segments automatically from online debate portals. Experiments across domains and registers show that training on such a corpus improves the effectiveness and robustness of mining argumentative text. We freely provide the underlying corpus for research.
In addition, REF used a distant supervision approach trained over debate portals' data, to develop a classifier for argumentative texts stored in these portals.
Cross-Domain Mining of Argumentative Text through Distant Supervision
HLT-NAACL
Computer Science