Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20 of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93 in the article-entity suggestion stage and upto 84 for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.
PART I. GOALS OF CENTERING PART II. UTTERANCE-LEVEL ISSUES IN CENTERING PART III. CENTERING AS A CROSS-LINGUISTIC UNIVERSAL PART IV. THE ROLE OF CENTERING IN PROCESSING MODELS OF DISCOURSE PART V. INFORMATION STRUCTURE AND CENTERING PART VI. DISCOURSE STRUCTURE AND CENTERING The research focus of computational coreference resolution has exhibited a shift from heuristic approaches to machine learning approaches in the past decade. This paper surveys the major milestones in supervised coreference research since its inception fifteen years ago. Although many NLP systems are moving toward entity-based processing, most still identify important phrases using classical keyword-based approaches. To bridge this gap, we introduce the task of entity salience: assigning a relevance score to each entity in a document. We demonstrate how a labeled corpus for the task can be automatically generated from a corpus of documents and accompanying abstracts. We then show how a classifier with features derived from a standard NLP pipeline outperforms a strong baseline by 34 . Finally, we outline initial experiments on further improving accuracy by leveraging background knowledge about the relationships between entities.