Papers with SeVeN
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors (C18-1)
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| Challenge: | Word embeddings use fixed-dimensional vectors to represent the meaning of words. |
| Approach: | They propose a pipeline for learning relation vectors based on word vector averaging and an ad hoc autoencoder. |
| Outcome: | The proposed pipeline can capture aspects of word meaning complementary to word embeddings. |