Name
Title | ||
|---|---|---|
A critique of word similarity as a method for evaluating distributional semantic models. |
Abstract | ||
|---|---|---|
This paper aims to re-think the role of the word similarity task in distributional semantics research. We argue while it is a valuable tool, it should be used with care because it provides only an approximate measure of the quality of a distributional model. Word similarity evaluations assume there exists a single notion of similarity that is independent of a particular application. Further, the small size and low inter-annotator agreement of existing data sets makes it challenging to find significant differences between models. |
Year | Venue | Field |
|---|---|---|
2016 | RepEval@ACL | Semantic similarity,Data set,Existential quantification,Computer science,Distributional semantics,Natural language processing,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miroslav Batchkarov | 1 | 0 | 0.34 |
| thomas kober | 2 | 4 | 2.44 |
| Jeremy Reffin | 3 | 14 | 3.32 |
| Julie Weeds | 4 | 541 | 34.97 |
| David J. Weir | 5 | 840 | 83.84 |