EMNLP 2019 (long). Github . Harvard Kennedy School's Institute of Politics Recommended for you 1:24:23 Lunch with a Trojan - USC Heisman winning quarterback Matt Leinart - Duration: 54:10. Secondly, we pre-train a LDA model on a set of 10,000 Arabic news articles—reporting MANSA events—which were used to generate the ground truth event dataset, and repeat the temporal feature generation on the entire Arabia Inform corpus,In order to generate daily features given a trained topic model and a set of time-stamped documents, we denote We hypothesize that the number of activities performed by an actor might have hidden structures (e.g., high-activity and low-activity periods), which may not be well-captured using a simple counting process, such as the Poisson process. These typologies, for the most part, are not derived from a theoretical basis, which prevents the reapplication of a given typology to another domain. Researchers have extensively studied and used time series analysis in many domains, such as finance,The generation of temporal features from text corpora for event forecasting is a diverse practice in the prediction of civil unrest,Emphasizing the role news articles can play as precursors to particular events, Ning et al.Forecasting military events has gained attention in recent years, as datasets have become more available.
His research interests are in the areas of computational social science, interpretable machine learning, and natural language processing. She hopes to pursue a graduate degree in the future. By continuing to browse We analyze the topics selected by the algorithm. Aida also works at the USC Institute of Armenian Studies. Outside of academics, Gwenyth can be found hiking and exploring Los Angeles.Shreya is a sophomore double majoring in Computer Science and Applied Math. Some of the countries (e.g., Saudi Arabia and Yemen) and most of the cities in our dataset have low event density, for which the HMM and the autoregressive models seem inadequate. We hypothesized that differences i...Acts of hate have been used to silence, terrorize, and erase marginalized social groups throughout history. We then shift the training period by a month and repeat the forecasting up to the month of September 2017. In When event density is low and event type is rare, it poses a challenge to our proposed models for predicting events in such settings. As an example, The proposed models essentially forecast event counts, but an intelligence analyst may need more details about the events for better understanding and dissemination. http://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( Outside of CSSL, Nikki works in the USC Health, Emotion, and Addiction Laboratory and is on the executive board of Interaxon, a group that shares its passion for neuroscience with local K-12 students. Project Github. Similar to country-level event data, we use six months of the GSR as training data starting from 1 August 2016, and use the model for predicting over a month, where there is a gap of a month between training and forecasting spans. This has unknown consequences for equity, particularly when a single set of linguistic stimuli are used for a diverse population of language users.
Brendan Kelly was introduced as the new USC Upstate Chancellor Tuesday morning. Find out about Lean Library Research off-campus without worrying about access issues. Assad Oberai AME, USC Verified email at usc.edu.
Please check you selected the correct society from the list and entered the user name and password you use to log in to your society website.Data-based computational approaches to forecasting political violenceHidden Markov models for the activity profile of terrorist groupsSelf-exciting point process models of civilian deaths in IraqThe effectiveness of antiterrorism policies: a vector-autoregression-intervention analysisIs transnational terrorism becoming more threatening? He has received his bachelor's degree from M S Ramaiah Institute of Technology, India, in Computer Science. B.S.
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