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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 – 18 of 18 1 Fairness-aware Class Imbalanced Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Baldwin, Timothy; Cohn, Trevor. - : Underline Science Inc., 2021 BASE Show details 2 Balancing out Bias: Achieving Fairness Through Training Reweighting ... Han, Xudong; Baldwin, Timothy; Cohn, Trevor. - : arXiv, 2021 BASE Show details 3 ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents He, Jiayuan; Nguyen, Dat Quoc; Akhondi, Saber A.... In: Front Res Metr Anal (2021) BASE Show details 4 Contextualization of Morphological Inflection ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 5 Target Based Speech Act Classification in Political Campaign Text ... Subramanian, Shivashankar; Cohn, Trevor; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 6 Twitter geolocation using knowledge-based methods Miyazaki, Taro; Rahimi, Afshin; Cohn, Trevor. - : Association for Computational Linguistics, 2018 BASE Show details 7 Semi-supervised User Geolocation via Graph Convolutional Networks Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : Association for Computational Linguistics -ACL, 2018 BASE Show details 8 A Neural Model for User Geolocation and Lexical Dialectology ... Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : arXiv, 2017 BASE Show details 9 Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks ... Rahimi, Afshin; Baldwin, Timothy; Cohn, Trevor. - : arXiv, 2017 BASE Show details 10 Topically Driven Neural Language Model ... Lau, Jey Han; Baldwin, Timothy; Cohn, Trevor. - : arXiv, 2017 BASE Show details 11 Context-Aware Prediction of Derivational Word-forms ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2017 BASE Show details 12 A neural model for user geolocation and lexical dialectology Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : Association for Computational Linguistics -ACL, 2017 BASE Show details 13 Continuous representation of location for geolocation and lexical dialectology using mixture density networks Rahimi, Afshin; Baldwin, Timothy; Cohn, Trevor. - : Association for Computational Linguistics, 2017 BASE Show details 14 Pigeo: A Python geotagging tool Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : Association for Computational Linguistics (ACL), 2016 BASE Show details 15 Twitter User Geolocation Using a Unified Text and Network Prediction Model ... Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : arXiv, 2015 BASE Show details 16 Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning ... Vylomova, Ekaterina; Rimell, Laura; Cohn, Trevor. - : arXiv, 2015 BASE Show details 17 Twitter user geolocation using a unified text and network prediction model Rahimi, Afshin; Cohn, Trevor; Baldwin, Timothy. - : Association for Computational Linguistics (ACL), 2015 BASE Show details 18 Exploiting text and network context for geolocation of social media users Rahimi, Afshin; Vu, Duy; Cohn, Trevor; Baldwin, Timothy. - : Association for Computational Linguistics (ACL), 2015 Abstract: Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no benchmarking of the two approaches over comparable datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text-and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets. Keyword: 1203 Language and Linguistics; 1706 Computer Science Applications; 3310 Linguistics and Language URL: https://espace.library.uq.edu.au/view/UQ:bdef91d BASE Hide details

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