Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information (D18-1)
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| Challenge: | a new method for text classification ignores strong non-linguistic similarities like homophily . authors are typically represented via their linguistic profiles, i.e. information avail-able in the text . |
| Approach: | They use homophily cues to retrofit text-based author representations with non-linguistic information and introduce a trade-off parameter. |
| Outcome: | The proposed method improves on two author-attribute prediction tasks with large labels. |
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