تقرير
Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification
العنوان: | Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification |
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المؤلفون: | Sinha, Vaibhav B, Rao, Sukrut, Balasubramanian, Vineeth N |
سنة النشر: | 2018 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
الوصف: | Many real world problems can now be effectively solved using supervised machine learning. A major roadblock is often the lack of an adequate quantity of labeled data for training. A possible solution is to assign the task of labeling data to a crowd, and then infer the true label using aggregation methods. A well-known approach for aggregation is the Dawid-Skene (DS) algorithm, which is based on the principle of Expectation-Maximization (EM). We propose a new simple, yet effective, EM-based algorithm, which can be interpreted as a `hard' version of DS, that allows much faster convergence while maintaining similar accuracy in aggregation. We show the use of this algorithm as a quick and effective technique for online, real-time sentiment annotation. We also prove that our algorithm converges to the estimated labels at a linear rate. Our experiments on standard datasets show a significant speedup in time taken for aggregation - upto $\sim$8x over Dawid-Skene and $\sim$6x over other fast EM methods, at competitive accuracy performance. The code for the implementation of the algorithms can be found at https://github.com/GoodDeeds/Fast-Dawid-Skene Comment: 8 pages, 5 tables, 1 figure, KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) 2018 |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/1803.02781 |
رقم الأكسشن: | edsarx.1803.02781 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |