Textual data are an important information source for risk management for business organizations. To effectively recognize, extract, and analyze risk-related statements in textual data, these processes need to be automated. We developed a design framework for firm-specific risk statements guided by previous economic, managerial, and natural language processing research. Four information types (r...

A reader of a news article would often be interested in the comments of other readers on anarticle, because comments give insight into popular opinions or feelings toward a given piece of news. In recent years, social media platforms, such as Twitter, have become a social hub for users to communicate and express their thoughts. This includes sharing news articles and commenting on them. In this...

New methods are needed for accessing very large information services. This paper proposes the use of a user model neural network to allow better access to a news service. The network is constructed on the basis of articles read, and articles marked as rejected. It adapts over time to better represent the user’s interests and rank the articles supplied by the news service. Using an augmented key...

Newspapers have separate sections for opinion articles and news articles. The goal of this project is to classify articles as opinion versus news and also to do analysis of the results to figure out the factors that distinguish the two. Preliminary results show that in this is possible with unigram features in an SVM with F1 of .90.

Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wiki pages) and focused on the coherence of the ...

Relation extraction is the task of finding entities in text connected by semantic relations. Bootstrapping approaches to relation extraction have gained considerable attention in recent years. These approaches are built with an underlying assumption, that when a pair of words is known to be related in a specific way, sentences containing those words are likely to express that relationship. Ther...

Consuming news articles is an integral part of our daily lives and news agencies such as The Washington Post (WP) expend tremendous effort in providing high quality reading experiences for their readers. Journalists and editors are faced with the task of determining which articles will become popular so that they can efficiently allocate resources to support a better reading experience. The rea...

Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our an...

Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed suffering however from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. We are proposing the enhancement of standard kmeans algorithm using the external knowledge fro...

We introduce the novel task of automatically generating questions that are relevant to a text but do not appear in it. One motivating example of its application is for increasing user engagement around news articles by suggesting relevant comparable questions, such as “is Beyonce a better singer than Madonna?”, for the user to answer. We present the first algorithm for the task, which consists ...