  The first two tasks of the CLEF 2019 ProtestNews events focused on
distinguishing between protest and non-protest related news articles and
sentences in a binary classification task. Among the submissions, two well
performing models have been chosen in order to replace the existing word
embeddings word2vec and FastTest with ELMo and DistilBERT. Unlike bag of words
or earlier vector approaches, ELMo and DistilBERT represent words as a sequence
of vectors by capturing the meaning based on contextual information in the
text. Without changing the architecture of the original models other than the
word embeddings, the implementation of DistilBERT improved the performance
measured on the F1-Score of 0.66 compared to the FastText implementation.
DistilBERT also outperformed ELMo in both tasks and models. Cleaning the
datasets by removing stopwords and lemmatizing the words has been shown to make
the models more generalizable across different contexts when training on a
dataset with Indian news articles and evaluating the models on a dataset with
news articles from China.
