Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts

Abstract

Named Entity Recognition (NER) is a key Natural Language Processing task. However, most existing work on NER targets flat named entities (NEs) and ignores the recognition of nested structures, where entities can be enclosed within other NEs. Moreover, evaluation of Nested Named Entity Recognition (NNER) across domains remains challenging, mainly due to the limited availability of datasets. To address these gaps, we present EWT-NNER, a dataset covering five web domains annotated for nested named entities on top of the English Web Treebank (EWT). We present the corpus and an empirical evaluation, including transfer results from German and Danish. EWT-NNER is annotated for four major entity types, including suffixes for derivational entity markers and partial named entities, spanning a total of 12 classes. We envision the public release of EWT-NNER to encourage further research on nested NER, particularly on cross-lingual cross-domain evaluation.