Name
Abstract | ||
|---|---|---|
Distributional semantic models learn vector representations of words through the contexts they occur in. Although the choice of context (which often takes the form of a sliding window) has a direct influence on the resulting embeddings, the exact role of this model component is still not fully understood. This paper presents a systematic analysis of context windows based on a set of four distinct hyper-parameters. We train continuous Skip-Gram models on two English-language corpora for various combinations of these hyper-parameters, and evaluate them on both lexical similarity and analogy tasks. Notable experimental results are the positive impact of cross-sentential contexts and the surprisingly good performance of right-context windows. |
Year | Venue | DocType |
|---|---|---|
2017 | NODALIDA | Conference |
Volume | Citations | PageRank |
abs/1704.05781 | 1 | 0.35 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre Lison | 1 | 146 | 12.35 |
| Andrey Kutuzov | 2 | 22 | 14.55 |