CoNEREL: Collective Information Extraction in News Articles

Minh C. Phan, Aixin Sun

Abstract: We present CoNEREL, a system for collective named entity recognition and entity linking focusing on news articles and readers' comments. Different from other systems, CoNEREL processes articles and comments in batch mode, to make the best use of the shared contexts of multiple news stories and their comments. Particularly, a news article provides context for all its comments. To improve named entity recognition, CoNEREL utilizes co-reference of mentions to refine their class labels ( e.g. , person, location). To link the recognized entities to Wikipedia, our system implements Pair-Linking, a state-of-the-art entity linking algorithm. Furthermore, CoNEREL provides an interactive visualization of the Pair-Linking process. From the visualization, one can understand how Pair-Linking achieves decent linking performance through iterative evidence building, while being extremely fast and efficient. The graph formed by the Pair-Linking process naturally becomes a good summary of entity relations, making CoNEREL a useful tool to study the relationships between the entities mentioned in an article, as well as the ones that are discussed in its comments.