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TREMA-UNH at TREC 2020

Sumanta Kashyapi, Laura Dietz
2020 Text Retrieval Conference  
This notebook describes the submissions of team TREMA-UNH to the TREC Podcasts track. We participate in the summarization task of the track.  ...  Introduction This year, team TREMA-UNH from the University of New Hampshire, USA, participated in the summarization task of TREC Podcasts track.  ...  As the training dataset, we use the benchmarkY1 train split of TREC CAR year 1 dataset [5] .  ... 
dblp:conf/trec/KashyapiD20 fatcat:cny2v66pcvc4hmonclayhjy3eu

TREC CAsT 2019: The Conversational Assistance Track Overview [article]

Jeffrey Dalton, Chenyan Xiong, Jamie Callan
2020 arXiv   pre-print
The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine Reading COmprehension (MARCO) datasets.  ...  The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information Seeking (CIS) research and to create a large-scale reusable test collection for conversational  ...  UNH-trema-ecn Y automatic uogTr ug_1stprev3_sdm automatic TREMA-UNH UNH-trema-ent Y automatic uogTr ug_cedr_rerank Y automatic TREMA-UNH unh-trema-relco automatic uogTr ug_cont_lin Y  ... 
arXiv:2003.13624v1 fatcat:ful7udqmmvfcfom65oxc76dq24

DUTh at TREC 2020 Conversational Assistance Track

Michalis Fotiadis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis
2020 Text Retrieval Conference  
This paper describes the DUTh's participation in the TREC 2020 Conversational Assistance Track (CAsT) track.  ...  /TREMA-UNH/trec-car-tools www.tensorflow.org  ...  Introduction This is an overview of the Democritus University of Thrace (DUTh) retrieval runs submissions to the TREC 2020 Conversational Assistance Track(CAsT) 1 , which focuses on conversational question  ... 
dblp:conf/trec/FotiadisPSA20 fatcat:p4tgp2finbfrvpvlbt5daafplm

Overview of the TREC 2019 deep learning track [article]

Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Ellen M. Voorhees
2020 arXiv   pre-print
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime.  ...  It is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC-style blind evaluation and reusable test sets.  ...  0.2402 0.7036 0.5058 0.7490 0.3013 srchvrs_ps_run1 srchvrs fullrank trad 0.1902 0.5597 0.4990 0.7240 0.2972 bm25tuned_p BASELINE fullrank trad 0.2363 0.6850 0.4973 0.7472 0.2903 UNH_bm25 TREMA-UNH  ... 
arXiv:2003.07820v2 fatcat:a4wghnw6fzbmfe4m24lpgpuwhy

BERT-ER

Shubham Chatterjee, Laura Dietz
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
We show that our entity ranking system using BERT-ER can increase precision at the top of the ranking by promoting relevant entities to the top.  ...  https://www.cs.unh.edu/~dietz/eal-dataset-2020/ http://trec-car.cs.unh.edu https://github.com/iai-group/DBpedia-Entity 6 https://github.com/TREMA-UNH/DBpediaV2-entity-CAR 7 https://www.cs.unh.edu  ...  For example, BERT-LeadText places the relevant entity "Organic Consumers Association" at rank 57 whereas BERT-SupportPsg places it at rank 13 (see Figure 4 ).  ... 
doi:10.1145/3477495.3531944 fatcat:7qwg5hir6bedfhcyqyttnomemu