TY - JOUR A1 - Romanello, Marina A1 - Walawender, Maria A1 - Hsu, Shih-Che A1 - Moskeland, Annalyse A1 - Palmeiro-Silva, Yasna A1 - Scamman, Daniel A1 - Smallcombe, James W A1 - Abdullah, Sabah A1 - Ades, Melanie A1 - Al-Maruf, Abdullah A1 - Ameli, Nadia A1 - Angelova, Denitsa A1 - Ayeb-Karlsson, Sonja A1 - Ballester, Joan A1 - Basagaña, Xavier A1 - Bechara, Hannah A1 - Beggs, Paul J A1 - Cai, Wenjia A1 - Campbell-Lendrum, Diarmid A1 - Charnley, Gina E C A1 - Courtenay, Orin A1 - Cross, Troy J A1 - Dalin, Carole A1 - Dasandi, Niheer A1 - Dasgupta, Shouro A1 - Davies, Michael A1 - Eckelman, Matthew A1 - Freyberg, Chris A1 - Garcia Corral, Paulina A1 - Gasparyan, Olga A1 - Giguere, Joseph A1 - Gordon-Strachan, Georgiana A1 - Gumy, Sophie A1 - Gunther, Samuel H A1 - Hamilton, Ian A1 - Hang, Yun A1 - Hänninen, Risto A1 - Hartinger, Stella A1 - He, Kehan A1 - Heidecke, Julian A1 - Hess, Jeremy J A1 - Jankin, Slava A1 - Jay, Ollie A1 - Pantera, Dafni Kalatzi A1 - Kelman, Ilan A1 - Kennard, Harry A1 - Kiesewetter, Gregor A1 - Kinney, Patrick A1 - Kniveton, Dominic A1 - Koubi, Vally A1 - Kouznetsov, Rostislav A1 - Lampard, Pete A1 - Lee, Jason K W A1 - Lemke, Bruno A1 - Li, Bo A1 - Linke, Andrew A1 - Liu, Yang A1 - Liu, Zhao A1 - Lowe, Rachel A1 - Ma, Siqi A1 - Mabhaudhi, Tafadzwanashe A1 - Maia, Carla A1 - Markandya, Anil A1 - Martin, Greta A1 - Martinez-Urtaza, Jaime A1 - Maslin, Mark A1 - McAllister, Lucy A1 - McMichael, Celia A1 - Mi, Zhifu A1 - Milner, James A1 - Minor, Kelton A1 - Minx, Jan A1 - Mohajeri, Nahid A1 - Momen, Natalie C A1 - Moradi-Lakeh, Maziar A1 - Morrisey, Karyn A1 - Munzert, Simon A1 - Murray, Kris A A1 - Obradovich, Nick A1 - Orgen, Papa A1 - Otto, Matthias A1 - Owfi, Fereidoon A1 - Pearman, Olivia L A1 - Pega, Frank A1 - Pershing, Andrew J A1 - Pinho-Gomes, Ana-Catarina A1 - Ponmattam, Jamie A1 - Rabbaniha, Mahnaz A1 - Repke, Tim A1 - Roa, Jorge A1 - Robinson, Elizabeth A1 - Rocklöv, Joacim A1 - Rojas-Rueda, David A1 - Ruiz-Cabrejos, Jorge A1 - Rusticucci, Matilde A1 - Salas, Renee N A1 - San José Plana, Adrià A1 - Semenza, Jan C A1 - Sherman, Jodi D A1 - Shumake-Guillemot, Joy A1 - Singh, Pratik A1 - Sjödin, Henrik A1 - Smith, Matthew R A1 - Sofiev, Mikhail A1 - Sorensen, Cecilia A1 - Springmann, Marco A1 - Stowell, Jennifer D A1 - Tabatabaei, Meisam A1 - Tartarini, Federico A1 - Taylor, Jonathon A1 - Tonne, Cathryn A1 - Treskova, Marina A1 - Trinanes, Joaquin A A1 - Uppstu, Andreas A1 - Valdes-Ortega, Nicolas A1 - Wagner, Fabian A1 - Watts, Nick A1 - Whitcombe, Hannah A1 - Wood, Richard A1 - Yang, Pu A1 - Zhang, Ying A1 - Zhang, Shaohui A1 - Zhang, Chi A1 - Zhang, Shihui A1 - Zhu, Qiao A1 - Gong, Peng A1 - Montgomery, Hugh A1 - Costello, Anthony T1 - The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline JF - The Lancet Y1 - 2025 U6 - https://doi.org/10.1016/S0140-6736(25)01919-1 VL - 406 IS - 10521 SP - 2804 EP - 2857 PB - Elsevier BV ER - TY - JOUR A1 - Béchara, Hannah A1 - Herzog, Alexander A1 - Jankin, Slava T1 - Transfer learning for topic labeling: Analysis of the UK House of Commons speeches 1935–2014 JF - Research and Politics N2 - Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated. Y1 - 2021 U6 - https://doi.org/10.1177/20531680211022206 VL - April-June 2021 ER - TY - JOUR A1 - Weeks, Liam A1 - Jankin Mikhaylov, Slava A1 - Herzog, Alex A1 - Ó Fathartaigh, Mícheál A1 - Bechara, Hannah T1 - It’s Only Words? Analysing the Roots of the Irish Party System Using Historical Parliamentary Debates JF - Parliamentary Affairs N2 - While the public image of legislative debates is often less than favourable, parliamentary deliberations can be an important indicator of policy preferences, issue saliency and cohesion within political parties. We consider the case of a parliamentary debate that had a considerable long-term political legacy, forging a party system that endured for almost a century. The debates in the Irish parliament over the 1921 Anglo-Irish Treaty were a critical juncture that split a dominant party, resulting in, first, a civil war and, later, a new mode of party competition. We analyse the text of the debates from this period to see if they contribute to a greater understanding of the ensuing split. Few differences between the two sides in parliament are found, which might explain why few were the differences between the key actors in the party system that evolved. Y1 - 2022 U6 - https://doi.org/10.1093/pa/gsac020 SN - 0031-2290 N1 - Published 29 September 2022 Weeks, L., Jankin Mikhaylov, S., Herzog, A., Ó Fathartaigh, M., & Bechara, H. (2023). It’s Only Words? Analysing the Roots of the Irish Party System Using Historical Parliamentary Debates. Parliamentary Affairs, 76(4), 836–856. https://doi.org/10.1093/pa/gsac020 ER - TY - JOUR A1 - van Daalen, Kim R A1 - Romanello, Marina A1 - Rocklöv, Joacim A1 - Semenza, Jan C A1 - Tonne, Cathryn A1 - Markandya, Anil A1 - Dasandi, Niheer A1 - Jankin, Slava A1 - Achebak, Hicham A1 - Ballester, Joan A1 - Bechara, Hannah A1 - Callaghan, Max W A1 - Chambers, Jonathan A1 - Dasgupta, Shouro A1 - Drummond, Paul A1 - Farooq, Zia A1 - Gasparyan, Olga A1 - Gonzalez-Reviriego, Nube A1 - Hamilton, Ian A1 - Hänninen, Risto A1 - Kazmierczak, Aleksandra A1 - Kendrovski, Vladimir A1 - Kennard, Harry A1 - Kiesewetter, Gregor A1 - Lloyd, Simon J A1 - Lotto Batista, Martin A1 - Martinez-Urtaza, Jaime A1 - Milà, Carles A1 - Minx, Jan C A1 - Nieuwenhuijsen, Mark A1 - Palamarchuk, Julia A1 - Quijal-Zamorano, Marcos A1 - Robinson, Elizabeth J Z A1 - Scamman, Daniel A1 - Schmoll, Oliver A1 - Sewe, Maquins Odhiambo A1 - Sjödin, Henrik A1 - Sofiev, Mikhail A1 - Solaraju-Murali, Balakrishnan A1 - Springmann, Marco A1 - Triñanes, Joaquin A1 - Anto, Josep M A1 - Nilsson, Maria A1 - Lowe, Rachel T1 - The 2022 Europe report of the Lancet Countdown on health and climate change: towards a climate resilient future JF - The Lancet Public Health Y1 - 2022 U6 - https://doi.org/10.1016/s2468-2667(22)00197-9 SN - 2468-2667 VL - 7 IS - 11 SP - E942 EP - E965 PB - Elsevier BV ER - TY - JOUR A1 - Béchara, Hannah A1 - Orăsan, Constantin A1 - Parra Escartín, Carla A1 - Zampieri, Marcos A1 - Lowe, William T1 - The Role of Machine Translation Quality Estimation in the Post-Editing Workflow JF - Informatics N2 - As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality. KW - machine translation quality estimation; post-editing Y1 - 2021 U6 - https://doi.org/10.3390/informatics8030061 VL - 8 IS - 3 ER -