Yeo and Z. Lei, Q. Yi, J.
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Wang and H. Wen, Traffic incident detection algorithm for urban expressways based on probe vehicle data, J. Lu, S. Chen, W. Wang and B. Pan, J. Kwok and Q. Pan and Q. Data Eng. Rossi, M. Gastaldi, G.
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Hinton and R. Williams, Learning representations by back-propagating errors, Nature , — Xiang, B.marpitisag.ml
Proceedings of ELM-2014 Volume 2
Cao, D. Hu and Q. Xu, H. Zhou and G. Yuan and R.
Cheu, Incident detection using support vector machines, Transport. Zhang and D. Image Process.
Journal of Intelligent Systems
Zhang, W. Zuo and D. Zong and G. Huang, Face recognition based on extreme learning machine, Neurocomputing 74 , — Export Citation. User Account Log in Register Help. Search Close Advanced Search Help. Show Summary Details. More options ….
- As the Future Catches You: How Genomics & Other Forces Are Changing Your Life, Work, Health & Wealth.
- Proceedings of ELM-2017.
- 1. Introduction.
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Volume 18 Issue 4 Dec , pp. Volume 17 Issue 4 Dec , pp. Also, we present a set of experiments, to better design the network parameters for the Twitter NER task. Our system is based on supervised machine learning by applying Conditional Random Fields CRF to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities.
The experimental results show that our method has significantly improved Twitter NER performance. These solutions might not lead to highly accurate results when being applied to noisy, user generated data, e. The models described in this paper are based on linear chain conditional random fields CRFs , use the BIEOU encoding scheme, and leverage random feature dropout for up-sampling the training data.
The considered features include word clusters and pre-trained distributed word representations, updated gazetteer features, and global context predictions. The latter feature allows for ingesting the meaning of new or rare tokens into the system via unsupervised learning and for alleviating the need to learn lexicon based features, which usually tend to be high dimensional.
We also present an improvement over our original submission [SI], which we built by using semi-supervised learning on labelled training data and pre-trained resourced constructed from unlabelled tweet data. Our ST solution achieved an F1 score of 1.
Neelam Dabas - Google Scholar Citations
The SI resulted in an increase of 8. We discuss details of task settings, data preparations and participant systems. The derived dataset and performance figures from each system provide baselines for future research in this realm. Our approach was to use ensemble methods to capitalise on four component methods: heuristics based on metadata, a label propagation method, timezone text classifiers, and an information retrieval approach. The ensembles we explored focused on examining the role of language technologies in geolocation prediction and also in examining the use of hard voting and cascading ensemble methods.
Furthermore, when estimating the latitude and longitude of a user, our median error distance was accurate to within 30 kilometers. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them.
In the test run of the task, the model achieved the accuracy of These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model. I start this talk by sketching some sample scenarios of Digital Humanities projects which involve various Humanities and Social Science disciplines, noting that the potential for a meaningful contribution to higher-level questions is highest when the employed language technological models are carefully tailored both a to characteristics of the given target corpus, and b to relevant analytical subtasks feeding the discipline-specific research questions.
Keeping up a multidisciplinary perspective, I then point out a recurrent dilemma in Digital Humanities projects that follow the conventional set-up of collaboration: to build high-quality computational models for the data, fixed analytical targets should be specified as early as possible — but to be able to respond to Humanities questions as they evolve over the course of analysis, the analytical machinery should be kept maximally flexible.
To reach both, I argue for a novel collaborative culture that rests on a more interleaved, continuous dialogue.
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