Named entity recognition architecture combining contextual and global features
This work addresses the challenge of accurately identifying named entities in text, which is crucial for simplifying information access, but it is incremental as it builds on existing methods.
The paper tackled the problem of named entity recognition by combining contextual features from XLNet with global features from Graph Convolution Networks to address context-dependency and misrepresentation of global relations, achieving results competitive with state-of-the-art on the CoNLL 2003 dataset.
Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations,...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context-dependent. While the context can be represented by contextual features, global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).