Name
Abstract | ||
|---|---|---|
The task of coreference resolution has attracted considerable attention in the literature due to its importance in deep language understanding and its potential as a subtask in a variety of complex natural language processing problems. In this study, we outlined the field's terminology, describe existing metrics, their differences and shortcomings, as well as the available corpora and external resources. We analyzed existing state-of-the-art models and approaches, and reviewed recent advances and trends in the field, namely end-to-end systems that jointly model different subtasks of coreference resolution, and cross-lingual systems that aim to overcome the challenges of less-resourced languages. Finally, we discussed the main challenges and open issues faced by coreference resolution systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/info11020074 | INFORMATION |
Keywords | Field | DocType |
natural language processing, coreference resolution, end-to-end systems, cross-language learning | Cross lingual,Coreference,Terminology,Computer science,End-to-end principle,Natural language processing,Artificial intelligence,Machine learning,Language understanding | Journal |
Volume | Issue | Citations |
11 | 2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Cruz | 1 | 0 | 2.03 |
| Gil Rocha | 2 | 2 | 2.75 |
| Henrique Lopes Cardoso | 3 | 223 | 34.02 |