Name
Abstract | ||
|---|---|---|
•Unsupervised word sense disambiguation technique for diverse NLP tasks.•Word embeddings combining sense disambiguation, improving its vector representation.•Synset embedding models can help to leverage the disambiguation of polysemous words.•Multi-sense embedding models provide better representation than single-sense ones.•Recurrent synset-embedding models can improve the quality of word representations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.06.026 | Expert Systems with Applications |
Keywords | DocType | Volume |
Multi-sense embeddings,Natural language processing,Word similarity,Synset | Journal | 136 |
ISSN | Citations | PageRank |
0957-4174 | 1 | 0.63 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Terry L. Ruas | 1 | 14 | 5.82 |
| William I. Grosky | 2 | 1 | 1.98 |
| Akiko N. Aizawa | 3 | 678 | 120.63 |