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University of Glasgow Terrier Team at TREC 2016: Experiments in Contextual Suggestions
2016
Text Retrieval Conference
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For TREC 2016, we focus on tackling the challenges posed by the Contextual Suggestion by investigating the use of user-generated data (e.g. textual content of comments and venue's information) in location-based ...
We train learning-to-rank models using these features on the TREC 2015 Contextual Suggestion dataset. ...
INTRODUCTION Similar to previous years, for the batch experiment in TREC 2016, the Contextual Suggestion track asks participants to suggest a ranked list of venues to users, based upon their pro les and ...
dblp:conf/trec/ManotumruksaMO16
fatcat:v2ev5drtczdkvfuip4opulsa74
Lucene4IR
2017
SIGIR Forum
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The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and ...
to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how ...
We would also like to thank our speakers as well as Bloomberg, Flax, LucidWorks and the University of Strathclyde. ...
doi:10.1145/3053408.3053421
fatcat:vnf7dzbiorhzxfy7437b2l5jem
Pretrained Transformers for Text Ranking: BERT and Beyond
2021
Proceedings of the 14th ACM International Conference on Web Search and Data Mining
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The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. ...
Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. ...
Acknowledgements 129 Acknowledgements This research was supported in part by the Canada First Research Excellence Fund and the Natural Sciences and Engineering Research Council (NSERC) of Canada. ...
doi:10.1145/3437963.3441667
fatcat:6teqmlndtrgfvk5mneq5l7ecvq