Papers by Martin Arvidsson
Interpretable Word Embeddings via Informative Priors (D19-1)
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| Challenge: | Existing word embeddings lack interpretability and are unsupervised . this limitation limits their use within computational social science and digital humanities. |
| Approach: | They propose to use informative priors to create interpretable dimensions for probabilistic word embeddings using a priori model. |
| Outcome: | The proposed models capture latent semantic concepts better than or on-par with the current state of the art while maintaining the simplicity and generalizability of priors. |