Multilingual Epidemiological Text Classification: A Comparative Study

[3], we combined the analysis of documents in the source and the target languages, with Multi- Sentence Compression (MSC) to generate more informative summaries.. We ex- panded

For a Parzen Window classifier (top-most cells), probabilistic active learning outperforms the other strategies significantly over all datasets, both af- ter 20 and 40 acquired

We report in Tables I and II the obtained classification accuracies and the required processing times for all the possible combinations of local key-point detectors and

Our findings reveal that a system consisting of Linear Normalization of the input image as data preprocessing step, HSV as feature extraction method and Balanced Random Forest

10: LIME and kernel SHAP saliency maps generated using 50 SLIC superpix- els with 5000 samples.. The other important parameter to set is the feature selection

We design a Recurrent Neural Network (RNN) using Residual Bidirectional Long Short-Term Memory (BiLSTM) layers with pretrained Embeddings for Language Models (ELMo) word

Text mining has become an established discipline in both research and business intelligence. It refers commonly to the method of extracting interesting information and

In this paper, we proposed a method for topic identification as a typical case of text categorization on Spanish tweets by using the deep representation of words.. Results