Improving Language-Dependent Named Entity Detection

Named Entity Recognition (NER) and Named Entity Linking (NEL) are two research areas that have shown big advancements in recent years. The majority of this research is based on the English language. Hence, some of these improvements are language-dependent and do not necessarily lead to better results when applied to other languages. Therefore, this paper discusses TOMO, an approach to language-aware named entity detection and evaluates it for the German language. This also required the development of a German gold standard dataset, which was based on the English dataset used by the OKE 2016 challenge. An evaluation of the named entity detection task using the web-based platform GERBIL was undertaken and results show that our approach produced higher F1 values than the other annotators did. This indicates that language-dependent features do improve the overall quality of the spotter.