Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints.Previous work has shown that modeling soft constraints, where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance.On the other hand, for imposing hard constraints, dual decomposition is a popular technique for efficient prediction given existing algorithms for unconstrained inference.We extend dual decomposition to perform prediction subject to soft constraints.Moreover, with a technique for performing inference given soft constraints, it is easy to automatically generate large families of constraints and learn their costs with a simple convex optimization problem during training.This allows us to obtain substantial gains in accuracy on a new, challenging citation extraction dataset.

This demonstration illustrates an information aggregation and summarization service for social sensing applications. Social sensing, using mobile phones and other networked devices in the possession of individuals, has gained significant popularity in recent years. In some cases, the information collected is structured, such as numeric data from temperature sensors, accelerometers, or GPS devices. Aggregate statistical properties, such as expected values, standard deviations, and outliers, can be easily computed, and can be used to summarize the data set. In other cases, however, the collection includes unstructured data types such as text or images with textual annotations. The concepts of expected values and outliers are harder to define, yet it is still important to be able to aggregate and summarize the data. We demonstrate a system which can automatically summarize real-time textual data common to social sensing applications. Specifically, we focus on text messages that describe events in the environment. The output of our service provides a reliable summary of observations that can be used in many contexts from military intelligence to participatory sensing campaigns.

Previous information extraction (IE) systems are typically organized as a pipeline architecture of separated stages which make independent local decisions. When the data grows beyond some certain size, the extracted facts become inter-dependent and thus we can take advantage of information redundancy to conduct reasoning across documents and improve the performance of IE. We describe a joint inference approach based on information network structure to conduct cross-fact reasoning with an integer linear programming framework. Without using any additional labeled data this new method obtained 13.7%-24.4% user browsing cost reduction over a state-of-the-art IE system which extracts various types of facts independently.