The rise of the internet and social media platforms has brought about significant changes in how people interact with each another. For a lot of people, the internet have also become the only source of news and information about the world. Thus due to the increase in accessibility of information, online sexism has also increased. Efforts should be made to make the internet a safe space for everyone, irrespective of gender, both from a larger social norms perspective and legal or technical regulations to help alleviate online gender-based violence. As a part of this, this paper explores simple methods that can be easily deployed to automatically detect online sexism in textual statements.

Using technology for analysis of human emotion is a relatively nascent research area. There are several types of data where emotion recognition can be employed, such as - text, images, audio and video. In this paper, the focus is on emotion recognition in text data. Emotion recognition in text can be performed from both written comments and from conversations. In this paper, the dataset used for emotion recognition is a list of comments. While extensive research is being performed in this area, the language of the text plays a very important role. In this work, the focus is on the Dravidian language of Tamil. The language and its script demands an extensive pre-processing. The paper contributes to this by adapting various pre-processing methods to the Dravidian Language of Tamil. A CNN method has been adopted for the task at hand. The proposed method has achieved a comparable result.

Title: JudithJeyafreedaAndrew@DravidianLangTech-EACL2021:Offensive language detection for Dravidian Code-mixed YouTube comments Author: Judith Jeyafreeda Andrew Messaging online has become one of the major ways of communication. At this level, there are cases of online/digital bullying. These include rants, taunts, and offensive phrases. Thus the identification of offensive language on the internet is a very essential task. In this paper, the task of offensive language detection on YouTube comments from the Dravidian lan- guages of Tamil, Malayalam and Kannada are seen upon as a mutliclass classification prob- lem. After being subjected to language spe- cific pre-processing, several Machine Learn- ing algorithms have been trained for the task at hand. The paper presents the accuracy results on the development datasets for all Machine Learning models that have been used and fi- nally presents the weighted average scores for the test set when using the best performing Ma- chine Learning model.

In recent years, the journalists and computer sciences speak to each other to identify useful technologies which would help them in extracting useful information. This is called “computational Journalism”. In this paper, we present a method that will enable the journalists to automatically identifies and annotates entities such as names of people, organizations, role and functions of people in legal documents; the relationship between these entities are also explored. The system uses a combination of both statistical and rule based technique. The statistical method used is Conditional Random Fields and for the rule based technique, document and language specific regular expressions are used.