Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mi...

We propose a real-time machine translation system that allows users to select a news category and to translate the related live news articles from Arabic, Czech, Danish, Farsi, French, German, Italian, Polish, Portuguese, Spanish and Turkish into English. The Moses-based system was optimised for the news domain and differs from other available systems in four ways: (1) News items are automatica...

In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.

This paper proposes a novel approach to sentiment analysis that leverages work in sociology on symbolic interactionism. The proposed approach uses Affect Control Theory (ACT) to analyze readers’ sentiment towards factual (objective) content and towards its entities (subject and object). ACT is a theory of affective reasoning that uses empirically derived equations to predict the sentiments and ...

1 NSContrast:Interface for Contrasting Multiple News Site Today, there is access to large numbers of news sites in different countries, and there are several experimental systems that integrate news articles about a particular event from multiple news sites. These systems enable a good understanding of particular events by using multiple news sites, but they ignore the characteristics of the va...

Generating a coherent synopsis and revealing the development threads for news stories from the increasing amounts of news content remains a formidable challenge. In this paper, we proposed a hddCRP (hybird distant-dependent Chinese Restaurant Process) based HierARChical tOpic model for news Article cLustering, abbreviated as CHARCOAL. Given a bunch of news articles, the outcome of CHARCOAL is t...

We present initial results of our effort to build an extensive and complete taxonomy of events described in news articles. By crawling Wikipedia’s current events portal we identified nine top–level event types. Using articles referenced by the portal we built a event type classification model for news articles using lexical and semantic features and present a small–scale manual evaluation of it...

Compact representation is a key issue for effective information delivery to users in mobile content-providing services. In particular, it is more severe when providing text documents such as news articles on the mobile service. Here we propose a method for generating compact image-based contents from news documents (News2Image). The proposed method consists of three modules for summarizing news...

Recent increase in digitalization and archiving efforts on news data have led to overwhelming amount of online information for a general user, thus making it difficult for them to retrospect on past events. One dimension along which past events can be effectively organized is time. Motivated by this idea, we introduce EXPOSÉ, an exploratory search system that explicitly uses temporal informatio...

Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and ca...