Name
Abstract | ||
|---|---|---|
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, the 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). |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-91669-5_21 | TOWARDS OPEN AND TRUSTWORTHY DIGITAL SOCIETIES, ICADL 2021 |
Keywords | DocType | Volume |
NER, XLNet, GCN, Contextual embeddings, Global embeddings | Conference | 13133 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tran Thi Hong Hanh | 1 | 0 | 0.34 |
| Antoine Doucet | 2 | 0 | 0.68 |
| Nicolas Sidere | 3 | 0 | 0.34 |
| Jose G. Moreno | 4 | 0 | 0.34 |
| Senja Pollak | 5 | 0 | 0.68 |