activity
20162022
most citedDynamic Trade-Off Prediction in Multi-Stage Retrieval Systems

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20221 cited

Early Stage Sparse Retrieval with Entity Linking

Dahlia Shehata, Negar Arabzadeh, Charles L. A. Clarke

Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations…

cs.IR20221 cited

Unsupervised Question Clarity Prediction Through Retrieved Item Coherency

Negar Arabzadeh, Mahsa Seifikar, Charles L. A. Clarke

Despite recent progress on conversational systems, they still do not perform smoothly and coherently when faced with ambiguous requests. When questions are unclear, conversational…

cs.CL2021

Translating Human Mobility Forecasting through Natural Language Generation

Hao Xue, Flora D. Salim, Yongli Ren +1

Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical…

cs.IR2016

Ten Blue Links on Mars

Charles L. A. Clarke, Gordon V. Cormack, Jimmy Lin +1

This paper explores a simple question: How would we provide a high-quality search experience on Mars, where the fundamental physical limit is speed-of-light propagation delays on t…

cs.IR20161 cited

Dynamic Trade-Off Prediction in Multi-Stage Retrieval Systems

J. Shane Culpepper, Charles L. A. Clarke, Jimmy Lin

Modern multi-stage retrieval systems are comprised of a candidate generation stage followed by one or more reranking stages. In such an architecture, the quality of the final ranke…