Text Classification of Manifestos and COVID-19 Press Briefings using BERT and Convolutional Neural Networks

Autor:	Chatsiou, Kakia
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning 68T07 91F10 J.4 J.5
Druh dokumentu:	Working Paper
Popis:	We build a sentence-level political discourse classifier using existing human expert annotated corpora of political manifestos from the Manifestos Project (Volkens et al., 2020a) and applying them to a corpus ofCOVID-19Press Briefings (Chatsiou, 2020). We use manually annotated political manifestos as training data to train a local topic ConvolutionalNeural Network (CNN) classifier; then apply it to the COVID-19PressBriefings Corpus to automatically classify sentences in the test corpus.We report on a series of experiments with CNN trained on top of pre-trained embeddings for sentence-level classification tasks. We show thatCNN combined with transformers like BERT outperforms CNN combined with other embeddings (Word2Vec, Glove, ELMo) and that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training. Comment: 12 pages, 1 figure, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.10267 Zobrazit plný text záznamu View this record from Arxiv