ERIS

Please refer to: Valerio Basile, Stella Markantonatou, Vivian Stamou, Iakovi Alexiou, Georgia Apostolopoulou, Vana Archonti, Antonis Balas, Eleni Koutli, Maria Panagiotopoulou. To appear. ERIS: issues in using HurtLex and Best-Worst Scaling annotation to develop a lexical resource for Modern Greek offensive language detection. Elias Said-Hung & Julio Montero-Diaz (Eds) News Media and Hate Speech Promotion in Mediterranean Countries. IGI Publisher https://www.igi-global.com/publish/call-for-papers/call-details/626

Greek offensive lexicon

The lexicon is a curated version of Hurtlex (el). Please refer to the following two papers: Elisa Bassignana, Valerio Basile, Viviana Patti. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-It 2018). Vivian Stamou, Iakovi Alexiou, Antigone Klimi, Eleftheria Molou, Alexandra Saivanidou and Stella Markantonatou. Cleansing and expanding the HURTLEX(EL) with a multidimensional categorization of offensive words. 2022. Proceedings of the 6th Workshop on Online Abuse and Harms (WOAH 2022). NAACL 2022

A review on depression following major life transitions

Moustafa et al. (2019) discussed how major life transitions such as postpartum depression and menopause lead to depression in women. In this paper, we expand on the major life transitions discussed by Moustafa et al. (2019), and extend the discussion to other major life transitions (such as retirement and entry to/exit from a prison) that can also lead to depression, in line with the transition theory of depression and the existing literature. Furthermore, possible evidence-based treatments or solutions for managing the depression caused by these major life events are suggested, and critical gaps in the existing literature identified and discussed

Universal Dependencies 2.8.1

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008). Version 2.8.1 fixes a bug in 2.8 where a portion of the Dutch Alpino treebank was accidentally omitted

Universal Dependencies 2.7

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.10

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.3

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Universal Dependencies 2.11

Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008)

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)