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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Implementation of rapid rule out of myocardial infarction using high-sensitivity troponin : cross-sectional survey of English hospitals Thapa, S.; Wong, R.; Goodacre, S.. - : BMJ Publishing Group, 2020 BASE Show details 2 Delphi consensus reached to produce a decision tool for SelecTing Approaches forRapid Reviews (STARR) Pandor, A.; Kaltenthaler, E.; Martyn-St James, M.. - : Elsevier, 2019 BASE Show details 3 Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings ; Proceedings of the 27th International Conference on Computational Linguistics Shafieibavani, E; Chen, F; Wong, R. - : Association for Computational Linguistics, 2018 BASE Show details 4 A Graph-Theoretic Summary Evaluation for Rouge ; Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing Ebrahimi, M; Chen, F; Shafieibavani, E. - : Association for Computational Linguistics, 2018 BASE Show details 5 A Unified Neural Network Model for Geolocating Twitter Users ; Proceedings of the 22nd Conference on Computational Natural Language Learning Ebrahimi, M; Shafieibavani, E; Wong, R; Chen, F. - : Association for Computational Linguistics, 2018 Abstract: Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation. However, many users do not share their exact geographical coordinates due to reasons such as privacy concerns. The lack of explicit location information has motivated a growing body of research in recent years looking at different automatic ways of determining the user’s primary location. In this paper, we propose a unified user geolocation method which relies on a fusion of neural networks. Our joint model incorporates different types of available information including tweet text, user network, and metadata to predict users’ locations. Moreover, we utilize a bidirectional LSTM network augmented with an attention mechanism to identify the most location indicative words in textual con- tent of tweets. The experiments demonstrate that our approach achieves state-of-the-art performance over two Twitter benchmark geolocation datasets. We also conduct an ablation study to evaluate the contribution of each type of information in user geolocation performance. URL: https://aclanthology.info/events/conll-2018 http://handle.unsw.edu.au/1959.4/unsworks_55537 BASE Hide details 6 Appraising UMLS Coverage for Summarizing Medical Evidence wong, R; chen, F; Shafieibavani, E. - 2016 BASE Show details

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