56 research outputs found
The Role of Self-Construal, Values, and Religiosity on Judgements of the Reasonability of Lying
No full text
The Role of Self-Construal, Values, and Religiosity on Judgements of the Reasonability of Lying
No full text
Biological aspects of two coexisting indigenous and non-indigenous fish species in the Aegean Sea: Pagellus erythrinus vs. Nemipterus randalli
No full textYapici, Sercan/0000-0003-2288-5084WOS: 000504749700013Nowadays, the Mediterranean is a hotspot of biodiversity, characterized by changes in fish communities due to invasions. These invasions, mainly occurring through the entrance of species through the Suez Canal, a process called Lessepsian migration, has been increasing in the last 40 years. It is reported that, in Turkish seas, where 512 fish species are found, are 75 Lessepsian species. However, knowledge about the impact of Lessepsian species on native species is insufficient. This study aims to determine the bio-ecological characteristics and food interactions of a native Pagellus erythrinus and non-native Nemipterus randalli distributed in the Gokova Bay. In the monthly sampling survey, carried out between January 2016 and December 2016, 1698 N. randalli and 945 P. erythrinus individuals were collected. Length, weight, age, sex distributions and ratios, length-age, weight-age, length-weight relationships, condition factors, stomach contents and reproduction periods were examined to determine the interaction between species. According to results, the life span of P. erythrinus is longer than N. randalli in the Gokova Bay. Nevertheless, N. randalli grows faster than P. erythrinus. Reproduction periods of both two species show similarities. Food competition between species is found to be significantly high. Results of condition factors exhibit that N. randalli shows an increased ability to exploit the available food sources. Pagellus erythrinus displays strategies such as: early maturation, short reproduction period, reproduction in the deeper waters and batch spawning, to compete with N. randalli. With the invasive characteristics of N. randalli established a successful population in the Gokova Bay.Mugla Sitki Kocman University Scientific Research FundMugla Sitki Kocman University [BAP 13/120]This research is based on Ph.D. thesis of the corresponding author and was supported by Mugla Sitki Kocman University Scientific Research Fund (BAP 13/120). The authors would like to thank the anonymous reviewers for their suggestions and comments. Also, corresponding author is grateful to Mr. Rifat Tezel, Dr. Umit Acar, Mr. Murat Celik, Mr. Umut Uyan for their valuable contributions and efforts during the study
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language
No full textThe 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
- …
