In 21st century which witnesses a range of extraordinary technological improvements; along with the world's turning into global-village-like place, it is a vital need that a common communication network being built up as a result of the interaction among the people whose nation, culture, language and beliefs differ drastically. With the aim of meeting this need, developed and developing countries have started to perform various studies in order to spread their languages all around the world. Similarly, one of those countries is Turkey where many studies are conducted in favour of teaching Turkish as a foreign language systematically and effectively. It is obvious that most of the studies are generally centered upon 'learning environment', 'curriculum' or 'learning materials'. However, cognitive-psychological dimension of learning is ignored. During the process of speaking, reading or writing in the foreign language, learners frequently develop anxiety and desperation. With the high level of anxiety, success in language education decreases considerably. This is the case for the listening skill which has an important place in language development. Since learners need to understand messages clearly and thoroughly in order to response properly, anxiety during listening or communication disorders resulting from it will affect development of language skills, making it impossible for people to communicate healthily.
Bir dili konuşabilmek için gerekli olan temel ve en önemli unsur gelişmiş bir söz varlığıdır. Söz varlığının geliştirilmesi sürecinde öğrencilerin, kelime öğrenirken en çok kullandıkları öğrenme araçlarının başında ise sözlükler gelmektedir. Sözlük kullanımı sayesinde öğrenciler hem kelimeleri kendi bağlamları hem de konuşma ve yazma başta olmak üzere dilin temel beceri alanları içerisinde kullanmayı öğrenir. Bu bağlamda çalışmada, yabancı dil olarak Türkçe öğrenenlerin sözlük kullanımına ilişkin görüşleri, sınıf içinde ve dışında sözlük kullanımına yönelik görüş ve tercihleri araştırılmıştır. Alanyazında yabancı dil olarak Türkçe öğrenenlerin sözlük kullanımına ilişkin görüşlerini geniş kapsamlı şekilde ortaya koyan araştırma sayısı yok denecek kadar azdır. Betimsel tarama modelinin kullanıldığı bu araştırmanın çalışma grubunu Jagiellonian Üniversitesi, Gazi Üniversitesi ve İstanbul Aydın Üniversitesi Türkçe Öğretim, Uygulama ve Araştırma Merkezlerinde (TÖMER) 2018-2019 eğitim-öğretim yılında öğrenim gören 332 öğrenci oluşturmaktadır. Çalışmada veri toplama aracı olarak uzman görüşleri doğrultusunda, araştırmacılar tarafından geliştirilen öğrenci görüşleri anketi kullanılmış, veriler analiz edilerek yorumlanmıştır.
The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task which asks models to decide if a text contains HOF. This task is challenging because of the large variety of explicit and implicit ways to verbally attack a target person or group. In this paper, we investigate whether HOF detection can profit by taking into account the relationships between HOF and similar concepts: (a) HOF is related to sentiment analysis because hate speech is typically a negative statement and expresses a negative opinion; (b) it is related to emotion analysis, as expressed hate points to the author experiencing (or pretending to experience) anger while the addressees experience (or are intended to experience) fear. (c) Finally, one constituting element of HOF is the (explicit or implicit) mention of a targeted person or group. On this basis, we hypothesize that HOF detection shows improvements when being modeled jointly with these concepts, in a multi-task learning setup. We base our experiments on existing data sets for each of these concepts (sentiment, emotion, target of HOF) and evaluate our models as a participant (as team IMS-SINAI) in the HASOC FIRE 2021 English Subtask 1A: "Subtask 1A: Identifying Hate, offensive and profane content from the post". Based on model-selection experiments in which we consider multiple available resources and submissions to the shared task, we find that the combination of the CrowdFlower emotion corpus, the SemEval 2016 Sentiment Corpus, and the OffensEval 2019 target detection data leads to an F1 =.7947 in a multi-head multi-task learning model based on BERT, in comparison to .7895 of a plain BERT model. On the HASOC 2019 test data, this result is more substantial with an increase by 2pp in F1 (from 0.78 F1 to 0.8 F1) and a considerable increase in recall. Across both data sets (2019, 2021), the recall is particularly increased for the class of HOF (6pp for the 2019 data and 3pp for the 2021 data), showing that MTL with emotion, sentiment, and target identification is an appropriate approach for early warning systems that might be deployed in social media platforms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.