Name
Abstract | ||
|---|---|---|
State-of-the-art entity linkers achieve high accuracy scores with probabilistic methods. However, these scores should be considered in relation to the properties of the datasets they are evaluated on. Until now, there has not been a systematic investigation of the properties of entity linking datasets and their impact on system performance. In this paper we report on a series of hypotheses regarding the long tail phenomena in entity linking datasets, their interaction, and their impact on system performance. Our systematic study of these hypotheses shows that evaluation datasets mainly capture head entities and only incidentally cover data from the tail, thus encouraging systems to overfit to popular/frequent and non-ambiguous cases. We find the most difficult cases of entity linking among the infrequent candidates of ambiguous forms. With our findings, we hope to inspire future designs of both entity linking systems and evaluation datasets. To support this goal, we provide a list of recommended actions for better inclusion of tail cases. |
Year | Venue | Field |
|---|---|---|
2018 | COLING | Entity linking,Computer science,Probabilistic method,Natural language processing,Artificial intelligence,Overfitting,Long tail |
DocType | Volume | Citations |
Conference | C18-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Ilievski | 1 | 12 | 7.63 |
| Piek Vossen | 2 | 387 | 61.59 |
| Stefan Schlobach | 3 | 1032 | 84.15 |