![]() ![]() ENT Rank: Retrieving Entities for Topical Information Needs through Entity-Neighbor-Text Relations. Unsupervised Extraction of Market Moving Events with Neural Attention. Luciano Del Corro and Johannes Hoffart.Entity query feature expansion using knowledge base links. Jeffrey Dalton, Laura Dietz, and James Allan.Entity-centric summarization: generating text summaries for graph snippets. Xgboost: A scalable tree boosting system. Learning relatedness measures for entity linking. Diego Ceccarelli, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, and Salvatore Trani.PageRank as a function of the damping factor. Paolo Boldi, Massimo Santini, and Sebastiano Vigna.NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg. Joshua Bambrick, Minjie Xu, Andy Almonte, Igor Malioutov, Guim Perarnau, Vittorio Selo, and Iat Chong Chan.We find that the salience of a contextual entity and how coherent it is with respect to the news story are strong indicators of relevance in both unsupervised and supervised settings. Our methods improve over the strongest baseline in terms ofPrecision at 1 by 7% (unsupervised) and 13% (supervised). We compare our models on this novel task by using a new, purpose-built test collection created using crowdsourcing. Our second method is based on learning to rank and combines the intuitions behind the unsupervised model with signals derived from hand-crafted features in a supervised setting. The first one is fully unsupervised and based on Personalized PageRank, calculated over a trending entity-specific graph of other entities where the edges encode a notion of directional similarity based on embedded background knowledge. Some of them are more important than others in the context of the trending entity and we thus determine a ranking of entities according to how useful they are in contextualizing the trend.We propose two solutions for ranking contextual entities. We refer to these retrieved entities as contextual entities. In this work, we consider trends that correspond to an entity in a knowledge graph and introduce the new and as-yet unexplored task of identifying other entities that may help explain the "why" an entity is trending. Trends are those keywords, phrases, or names that are mentioned most often on social media or in news in a particular timeframe.They are an effective way for human news readers to both discover and stay focused on the most relevant information of the day. ![]()
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