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Distributed representations of words have shown to be useful to improve theeffectiveness of IR systems in many sub-tasks like query expansion, retrievaland ranking. Algorithms like word2vec, GloVe and others are also key factors inmany improvements in different NLP tasks. One common issue with such embeddingmodels is that words like happy and sad appear in similar contexts and henceare wrongly clustered close in the embedding space. In this paper we leverageAff2Vec, a set of word embeddings models which include affect information, inorder to better capture the affect aspect in news text to achieve betterresults in information retrieval tasks, also such embeddings are less hit bythe synonym/antonym issue. We evaluate their effectiveness on two IR relatedtasks (query expansion and ranking) over the New York Times dataset (TREC-core'17) comparing them against other word embeddings based models and classicranking models.

Affect Enriched Word Embeddings for News Information Retrieval