activity
19962018
most citedVisual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

73 citations · 146 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CV2018★ 73 cited

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Yuxing Tang, Josiah Wang, Xiaofang Wang +4

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large numbe…

cs.CL2012

A Data Driven Approach to Query Expansion in Question Answering

Leon Derczynski, Jun Wang, Robert Gaizauskas +1

Automated answering of natural language questions is an interesting and useful problem to solve. Question answering (QA) systems often perform information retrieval at an initial s…

cs.CL2012★ 14 cited

USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding

Amev Burman, Arun Jayapal, Sathish Kannan +4

This paper describes the University of Sheffield's entry in the 2011 TAC KBP entity linking and slot filling tasks. We chose to participate in the monolingual entity linking task,…

cs.CL2012★ 13 cited

A Corpus-based Study of Temporal Signals

Leon Derczynski, Robert Gaizauskas

Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are conveyed in text via va rious linguistic mechanisms inc…

cs.CL2012★ 6 cited

An Annotation Scheme for Reichenbach's Verbal Tense Structure

Leon Derczynski, Robert Gaizauskas

In this paper we present RTMML, a markup language for the tenses of verbs and temporal relations between verbs. There is a richness to tense in language that is not fully captured…

cs.CL2012

USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2

Leon Derczynski, Robert Gaizauskas

We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in te…