This paper analyzes two state-of-the-art Neural Information Retrieval (NeuIR) models: the
Deep Relevance Matching Model (DRMM) and the Neural Vector Space Model (NVSM). Our …

Recent neural ranking algorithms focus on learning semantic matching between query and
document terms. However, practical learning to rank systems typically rely on a wide range …

Abstract LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora
where a single relevance label is assigned to each available document‐topic pair. Within …

In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval
(IR). The semantic gap can be described as the mismatch between users' queries and the …

Deep neural models revolutionized the research landscape in the Information Retrieval (IR)
domain. Nowadays, they are employed at different stages in retrieval and ranking pipelines …

Deep neural models revolutionized the research landscape in the Information Retrieval (IR)
domain. Nowadays, they are employed at different stages in retrieval and ranking pipelines …

The field of information access is of vital importance in our modern societies, since most of
the information is now accessible in a digital form and is increasing in volume at a fast pace …