The Role of Global and Local Context in Named Entity Recognition
This addresses a bottleneck in NER for long documents like novels, but the approach is incremental as it builds on existing transformer-based methods without introducing a new paradigm.
The paper tackles the problem of Named Entity Recognition (NER) in long documents by investigating the impact of global versus local context, finding that correctly retrieving global context improves performance more than relying solely on local context.
Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately only incorporates local context and prevents leveraging global document context in long documents such as novels, which might hinder performance. In this article, we explore the impact of global document context, and its relationships with local context. We find that correctly retrieving global document context has a greater impact on performance than only leveraging local context, prompting for further research on how to better retrieve that context.