News event prediction by trigger evolution graph and event segment

Feature-Rich Segment-Based News Event Detection on Twitter

Event detection on Twitter is an important and challenging research topic. On the one hand, Twitter provides first-hand information and fast broadcasting. On the other, challenges include short and noisy content, big volume data and fast-changing topics. Dominant approaches for Twitter event detection model events by clustering tweets, words or segments, while segments have been proven to be ad...

Event Linking: Grounding Event Reference in a News Archive

Interpreting news requires identifying its constituent events. Events are complex linguistically and ontologically, so disambiguating their reference is challenging. We introduce event linking, which canonically labels an event reference with the article where it was first reported. This implicitly relaxes coreference to co-reporting, and will practically enable augmenting news archives with se...

Event Oriented Query Expansion for News Event Queries

Query expansion techniques have been commonly used by participants in Temporal Summarization tack. However, many previous attempts focused on expanding queries based on their event types, which only covers partially of the information need represented by queries. The reason is that the queries in the TS track are about news events, and for an event query, the event related entities, which are t...

Event Evolution and Archiving

The Temporal Event Graph

Temporal networks are increasingly being used to model the interactions of complex systems. Most studies require the temporal aggregation of edges (or events) into discrete time steps to perform analysis. In this article we describe a static, lossless, and unique representation of a temporal network, the temporal event graph (TEG). The TEG describes the temporal network in terms of both the int...