Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Jeffrey Dalton"'
Autor:
Raphael Underwood, Liam Mason, Owen O’Daly, Jeffrey Dalton, Andrew Simmons, Gareth J. Barker, Emmanuelle Peters, Veena Kumari
Publikováno v:
npj Schizophrenia, Vol 7, Iss 1, Pp 1-9 (2021)
Abstract Anomalous perceptual experiences are relatively common in the general population. Evidence indicates that the key to distinguishing individuals with persistent psychotic experiences (PEs) with a need for care from those without is how they a
Externí odkaz:
https://doaj.org/article/27208c0689484085a9561831b5710441
Autor:
Leon Fonville, Nick P Lao-Kaim, Vincent Giampietro, Frederique Van den Eynde, Helen Davies, Naima Lounes, Christopher Andrew, Jeffrey Dalton, Andrew Simmons, Steven C R Williams, Simon Baron-Cohen, Kate Tchanturia
Publikováno v:
PLoS ONE, Vol 8, Iss 5, p e63964 (2013)
The behavioural literature in anorexia nervosa and autism spectrum disorders has indicated an overlap in cognitive profiles. One such domain is the enhancement of local processing over global processing. While functional imaging studies of autism spe
Externí odkaz:
https://doaj.org/article/edad3a1cc61f4279abb05072cf936bef
Autor:
Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espin-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Kücük Mcginty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram Van Den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Ethan Yifan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu
Publikováno v:
Companion Proceedings of the ACM Web Conference 2023.
Autor:
Jeffrey Dalton, Sophie Fischer, Paul Owoicho, Filip Radlinski, Federico Rossetto, Johanne R. Trippas, Hamed Zamani
Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Autor:
Jeffrey Dalton, Laura Dietz
Publikováno v:
Datenbank-Spektrum. 20:17-28
Manually creating test collections is a time-, effort-, and cost-intensive process. This paper describes a fully automatic alternative for deriving large-scale test collections, where no human assessments are needed. The empirical experiments confirm
Publikováno v:
SIGIR
Understanding and comparing the behavior of retrieval models is a fundamental challenge that requires going beyond examining average effectiveness and per-query metrics, because these do not reveal key differences in how ranking models' behavior impa
Publikováno v:
Information Retrieval Journal, 25(2). Springer Netherlands
In this work, we study recent advances in context-sensitive language models for the task of query expansion. We study the behavior of existing and new approaches for lexical word-based expansion in both unsupervised and supervised contexts. For unsup
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030721121
ECIR (1)
ECIR (1)
In this work we leverage recent advances in context-sensitive language models to improve the task of query expansion. Contextualized word representation models, such as ELMo and BERT, are rapidly replacing static embedding models. We propose a new mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::317cbed89b406a974e02c61950757c4e
Publikováno v:
SIGIR
SIGIR 2021: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
SIGIR 2021: 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Deep Learning Hard (DL-HARD) is a new annotated dataset designed to moreeffectively evaluate neural ranking models on complex topics. It builds on TRECDeep Learning (DL) topics by extensively annotating them with question intentcategories, answer typ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68306c3986ed871d4a656126e6dce87c
https://hdl.handle.net/21.11116/0000-0009-67AB-321.11116/0000-0009-67AD-1
https://hdl.handle.net/21.11116/0000-0009-67AB-321.11116/0000-0009-67AD-1
Publikováno v:
SIGIR
Task-based Virtual Personal Assistants (VPAs) such as the Google Assistant, Alexa, and Siri are increasingly being adopted for a wide variety of tasks. These tasks are grounded in real-world entities and actions (e.g., book a hotel, organise a confer