Name
Abstract | ||
|---|---|---|
GloVe, global vectors for word representation, performs well in some word analogy and semantic relatedness tasks. However, we find that some dimensions of the trained word embedding are abnormal. We verify our conjecture via removing these abnormal dimensions using Kolmogorov-Smimov test and experiment on several benchmark datasets for semantic relatedness measurement. The experimental results confirm our finding. Interestingly, some of the tasks outperform the state-of-the-art model SensEmbed by simply removing these abnormal dimensions. The novel rule of thumb technique which leads to better performance is expected to be useful in practice.
|
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2872518.2889381 | WWW '16: 25th International World Wide Web Conference
Montréal
Québec
Canada
April, 2016 |
Field | DocType | ISBN |
Semantic similarity,Word representation,Computer science,Filter (signal processing),Artificial intelligence,Rule of thumb,Word embedding,Analogy,Conjecture | Conference | 978-1-4503-4144-8 |
Citations | PageRank | References |
2 | 0.43 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang-Yin Lee | 1 | 20 | 3.70 |
| Ke Hao | 2 | 22 | 4.08 |
| Hen-Hsen Huang | 3 | 63 | 37.14 |
| Hsin-hsi Chen | 4 | 2267 | 233.93 |