How to Use Gazetteers for Entity Recognition with Neural Models

Although the use of end-to-end neural architectures has been proven to be effective on several sequence labeling tasks, the use of gazetteers in these architectures is still rather unexplored. We investigate several options, aiming at exploiting gazetteers to extract relevant features, and then at integrating these features in a neural model for entity recognition. We provide experimental evidences on two datasets (named entities and nominal entities) and two languages (English and Italian), showing that extracting features from a rich model of the gazetteer and then concatenating such features with the input embeddings of a neural model is the best strategy in all our experimental settings, significantly outperforming more conventional approaches.