TF-IDF Character iV-grams versus Word Embedding-based Models for Fine-grained Event Classification: A Preliminary study

摘要

Automating the detection of event mentions in online texts and their classification vis-a-vis domain-specific event type taxonomies has been acknowledged by many organisations worldwide to be of paramount importance in order to facilitate the process of intelligence gathering. This paper reports on some preliminary experiments of comparing various linguistically-lightweight approaches for fine-grained event classification based on short text snippets reporting on events. In particular, we compare the performance of a TF-IDF-weighted character n-gram SVM-based model with SVMs trained on various off-the-shelf pre-trained word embeddings (GLOVE, BERT , FASTTEXT) as features. We exploit a relatively large event corpus consisting of circa 610K short text event descriptions classified using 25-event categories that cover political violence and protest events. The best results, i.e., 83.5% macro and 92.4% micro F_1 score, were obtained using the TF-IDF-weighted character n-gram model.