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Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution
This paper develops and implements a scalable methodology for (a) estimating the noisiness of labels produced by a typical crowdsourcing semantic annotation task, and (b) reducing the resulting error of the labeling process by as much as 20-30% in comparison to other common labeling strategies.
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making.
Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification
Auto dealerships receive thousands of calls daily from customers who are interested in sales, service, vendors and jobseekers.
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