176 citations · 914 across the 61 of their papers we have counts for
6 papers · 2 filters
Neural Ranking Models with Multiple Document Fields
Hamed Zamani, Bhaskar Mitra, Xia Song +2
Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering docu…
Reply With: Proactive Recommendation of Email Attachments
Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi +4
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterpr…
Toward Incorporation of Relevant Documents in word2vec
Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu +1
Recent advances in neural word embedding provide significant benefit to various information retrieval tasks. However as shown by recent studies, adapting the embedding models for t…
Benchmark for Complex Answer Retrieval
Federico Nanni, Bhaskar Mitra, Matt Magnusson +1
Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets…
Neural Models for Information Retrieval
Bhaskar Mitra, Nick Craswell
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ m…
Luandri: a Clean Lua Interface to the Indri Search Engine
Bhaskar Mitra, Fernando Diaz, Nick Craswell
In recent years, the information retrieval (IR) community has witnessed the first successful applications of deep neural network models to short-text matching and ad-hoc retrieval.…