This paper explores the possibility of using neural embedding features for enhancing the effectiveness of ad hoc document ranking based on learning to rank models. We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents. We employ several learning to rank methods for document ranking using embedding-based features, keyword-based features as well as the interpolation of the embedding-based features with keyword-based features. The results show that embedding features have a synergistic impact on keyword based features and are able to provide statistically significant improvement on harder queries.

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