Out-of-context fine-grained multi-word entity classification
Guillaume Jacquet, Jakub Piskorski, Sophie Chesney · 2019
In this paper, we present a number of experiments on the construction of fine-grained and out-of-context multi-word entity classification models. These models exploit a large BabelNet-derived multilingual Named Entity corpus of 49 languages from 7 different scripts, which is also presented in this work. In particular, we compare SVM-based character and token n-gram models with neural network-based ones and also explore language-specific variants against multilingual models. The various models have been evaluated on additional external Named Entity resources to gain further insight into the quality and re-usability of the trained models.