Name
Abstract | ||
|---|---|---|
We present a novel approach for computing similarity of English word pairs. While many previous approaches compute cosine similarity of individually computed word embeddings, we compute a single embedding for the word pair that is suited for similarity computation. Such embeddings are then used to train a machine learning model. Testing results on MEN and WordSim-353 datasets demonstrate that for the task of word pair similarity, computing word pair embeddings is better than computing word embeddings only. |
Year | Venue | Field |
|---|---|---|
2017 | WML@ICDAR | Embedding,Similarity computation,Cosine similarity,Task analysis,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Semantics |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nazar Khan | 1 | 15 | 6.38 |
| Asma Shaukat | 2 | 0 | 0.34 |