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Event Early Embedding

Zhiwei Liu, Yang Yang, Zi Huang, Fumin Shen, Dongxiang Zhang, Heng Tao Shen
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
However, this prediction is non-trivial because a) social events always stay with "noise" under the same topic and b) the information obtained at its early stage is too sparse and limited to support an  ...  Predicting the future dynamics of an event at a very early stage is significantly valuable, e.g, helping company anticipate marketing trends before the event becomes mature.  ...  Thus, we predict the future dynamics of events at early stage with both volume and content feature. Our predicting method is based on locally linear embedding algorithm [10] .  ... 
doi:10.1145/3077136.3080700 dblp:conf/sigir/Liu0HSZS17 fatcat:tyir5qntwvhnzmw7ws55byr7la

Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding [article]

Zhengping Che, Yu Cheng, Zhaonan Sun, Yan Liu
2017 arXiv   pre-print
Our initial experiments produce promising results and demonstrate the effectiveness of both the medical feature embedding and the proposed convolutional neural network in risk prediction on cohorts of  ...  In this paper, we explore deep neural network models with learned medical feature embedding to deal with the problems of high dimensionality and temporality.  ...  Early Prediction Making better prediction in advance can help doctor make timely decisions and diagnose diseases in their early stage. We also test our model in a simulated early stopping setting.  ... 
arXiv:1701.07474v1 fatcat:6p5ntv4e5jfszmah2trezqlxqm

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation [article]

Luchen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming Zhang
2019 arXiv   pre-print
However, the early prediction is challenging because patients' sequential data in EHR contains temporal interactions of multiple clinical events.  ...  Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and  ...  And the early prediction of the sepsis onset is important for physicians to take early preventive treatment.  ... 
arXiv:1910.06792v1 fatcat:uvuiyu2byrfn5axajpebuowf54

On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners [chapter]

Tu Ngoc Nguyen, Cheng Li, Claudia Niederée
2017 Lecture Notes in Computer Science  
For a better understanding, we also conduct an extensive feature evaluation that emphasized on the early stage and shows that the low-level credibility has best predictability at all phases of the rumor  ...  We then aggregate the predictions from the very beginning of a rumor to obtain the overall event credits (so-called wisdom), and finally combine it with a time series based rumor classification model.  ...  the rumor sub-events in the early stage of the event Munich shooting.  ... 
doi:10.1007/978-3-319-67256-4_13 fatcat:dpbgb5aj3jd7njopxl7zji2bvm

Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

Dongliang Liao, Jin Xu, Gongfu Li, Weijie Huang, Weiqing Liu, Jing Li
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles.  ...  Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval.  ...  Acknowledgment This work was done when Dongliang Liao was interning at WeChat, Tencent Inc.  ... 
doi:10.1609/aaai.v33i01.3301200 fatcat:cvxckyim4vfebhktvwtas7k264

CLEar

Runquan Xie, Feida Zhu, Hui Ma, Wei Xie, Chen Lin
2014 Proceedings of the VLDB Endowment  
Second, we demonstrate popularity prediction for the detected bursty topics and event summarization based on clustering related topics detected in successive time periods.  ...  Third, we illustrate CLEar's module for contextualizing and visualizing the event evolution both along time-line and across other news media to offer an easier understanding of the events.  ...  Popularity Prediction: Predict trending topic popularity, and remove both noisy and spam bursty topics at an early stage.  ... 
doi:10.14778/2733004.2733049 fatcat:prxravc4szc73nhrktoonhrvee

Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features

Zhigang Kan, Linbo Qiao, Sen Yang, Feng Liu, Feng Huang
2020 IEEE Access  
In pipelined structures, the difficulty of event arguments extraction lies in its lack of classification feature, and the much higher computation consumption.  ...  In addition, enhanced local information is incorporated into word features, to assign event arguments roles for triggers predicted by the first subtask.  ...  Early event extraction work mainly used pipeline structure with two stages, in which event trigger prediction applied at the first stage, and corresponding arguments classification conducted at the second  ... 
doi:10.1109/access.2020.3004378 fatcat:d2dyorwlebbvhpibtq7hqey5lq

Adaptive Prediction Timing for Electronic Health Records [article]

Jacob Deasy, Ari Ercole, Pietro Liò
2020 arXiv   pre-print
Our model predicts more frequently when events are dense or the model is certain of event latent representations, and less frequently when readings are sparse or the model is uncertain.  ...  We use a Recurrent Neural Network (RNN) and a Bayesian embedding layer with a new aggregation method to demonstrate adaptive prediction timing.  ...  This early-stage modelling decision-necessitated by traditional Recurrent Neural Network (RNN, Elman (1990) ) structure-loses patient information and timeseries granularity, and ignores the underlying  ... 
arXiv:2003.02554v1 fatcat:qzhqy53jzreernsn2k7oww6oc4

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomašev, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, Alistair Connell, Cían O. Hughes (+15 others)
2019 Nature  
Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2-17 and  ...  The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating  ...  Finally, we thank the many VA physicians, administrators and researchers who worked on the data collection, and the rest of the DeepMind team for their support, ideas and encouragement.  ... 
doi:10.1038/s41586-019-1390-1 pmid:31367026 pmcid:PMC6722431 fatcat:4fz4rieqqvh2zirmvah7ijgela

The Secant Rate of Corrosion: Correlating Observations of the USS Arizona Submerged in Pearl Harbor

Donald L. Johnson, Robert J. DeAngelis, Dana J. Medlin, Jon E. Johnson, James D. Carr, David L. Conlin
2018 JOM: The Member Journal of TMS  
The correlation yields a lower rate of metal thinning than predicted. Development of the correlation is described.  ...  Contrary to previous linear projections of steel corrosion in seawater, analysis of an inert marker embedded in USS Arizona concretion since the 7 December 1941 attack on Pearl Harbor reveals evidence  ...  Tim Christenson, DOD, for early review of the manuscript.  ... 
doi:10.1007/s11837-018-2797-0 fatcat:6ktyqmxwdzce3gd2qy7wmu2e4u

An interpretable deep-learning model for early prediction of sepsis in the emergency department

Dongdong Zhang, Changchang Yin, Katherine M. Hunold, Xiaoqian Jiang, Jeffrey M. Caterino, Ping Zhang
2021 Patterns  
A long short-term memory (LSTM)-based model with event embedding and time encoding is leveraged to model clinical time series and boost prediction performance.  ...  Our model achieved an average area under the curve of 0.892 and was selected as one of the winners of the challenge for both prediction accuracy and clinical interpretability.  ...  The authors declare no competing interests. Received: October 11, 2020 Revised: November 3, 2020 Accepted: December 18, 2020 Published: January 19, 2021  ... 
doi:10.1016/j.patter.2020.100196 pmid:33659912 pmcid:PMC7892361 fatcat:umchy57axfhbhislrq4cftx6li

Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features [article]

Zhigang Kan, Linbo Qiao, Sen Yang, Feng Liu, Feng Huang
2020 arXiv   pre-print
In pipelined structures, the difficulty of event arguments extraction lies in its lack of classification feature, and the much higher computation consumption.  ...  In addition, enhanced local information is incorporated into word features, to assign event arguments roles for triggers predicted by the first subtask.  ...  Early event extraction work mainly used pipeline structure with two stages, in which event trigger prediction applied at the first stage, and corresponding arguments classification conducted at the second  ... 
arXiv:2006.01854v1 fatcat:n42kpogubndmllbi6iv5y6rgxy

Exploring Emoji Usage and Prediction Through a Temporal Variation Lens [article]

Francesco Barbieri, Luis Marujo, Pradeep Karuturi, William Brendel, Horacio Saggion
2018 arXiv   pre-print
We compare emoji embeddings trained on a corpus of different seasons and show that some emojis are used differently depending on the time of the year.  ...  Moreover, we propose a method to take into account the time information for emoji prediction systems, outperforming state-of-the-art systems.  ...  Francesco B. and Horacio S. acknowledge support from the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).  ... 
arXiv:1805.00731v1 fatcat:pd3ocptlojfwbcbpkmfrbhivre

Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing

Jari Björne, Tapio Salakoski
2018 Proceedings of the BioNLP 2018 workshop  
Most notably, we encode the parse graph into this linear space using dependency path embeddings. We integrate our neural network into the open source Turku Event Extraction System (TEES) framework.  ...  Where relation extraction concerns the detection of semantic connections between pairs of entities, event extraction expands this concept with the addition of trigger words, multiple arguments and nested  ...  Event Argument Embeddings are used only in the unmerging stage where predicted entities and edges are divided into separate events.  ... 
doi:10.18653/v1/w18-2311 dblp:conf/bionlp/BjorneS18 fatcat:d3jjs3g4dngk5eh2z4sqjvsfzu

Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey [article]

Yuecai Zhu, Fuyuan Lyu, Chengming Hu, Xi Chen, Xue Liu
2022 arXiv   pre-print
However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them.  ...  In order to offer a convenient reference to both the industry and academia, this survey presents the Three Stages Recurrent Temporal Learning Framework based on dynamic graph evolution theories, so as  ...  Due to their pros and cons, the most important decision to make is which storage model to use. Such decision must be made at the early stage of a dynamic graph learning use case.  ... 
arXiv:2203.10480v2 fatcat:tf7n73rhtbbcpptbn6lyvhcew4
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