Affect Enriched Word Embeddings for News Information Retrieval
Table 5: Query expansion experiments on CACM
['Query expansion experiments on CACM Model', 'Query expansion experiments on CACM NDCG', 'Query expansion experiments on CACM MAP']
We observe that usage of term embeddings generally causes steadily higher NDCG and MAP. While we expected best results with Aff2Vec models it turned out ”vanilla” word2vec model trained on Google News corpus outperformed all the others in NDCG and MAP. On the other hand the best performing enriched model is a retrofitted word2vec model whereas among affect enriched models the GloVe retrofitted one provides the best results.