activity
20162018
most citedQuery Expansion with Locally-Trained Word Embeddings

54 citations · 85 across the 5 of their papers we have counts for

collaborators

7 papers

cs.IR2018

Neural Networks for Information Retrieval

Tom Kenter, Alexey Borisov, Christophe Van Gysel +3

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approac…

cs.IR20172 cited

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…

cs.IR201718 cited

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…

cs.IR20179 cited

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…

cs.IR2017

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…

cs.IR20172 cited

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.…