Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2020 - 20212

Settings

Author: search.filters.author.Premjith, B.

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Overview of the track on HASOC-offensive Language Identification-DravidianCodeMix
    (CEUR-WS, 2020) Chakravarthi, B.R.; Anand Kumar, M.; Mccrae, J.P.; Premjith, B.; Padannayil, K.P.; Mandl, T.
    We present the results and main findings of the HASOC-Offensive Language Identification on code mixed Dravidian languages. The task featured two tasks. Task 1 is about offensive language identification in Malayalam language where the comment were written in both native script and Latin script. Task 2 is about offensive language identification in Tamil and Malayalam languages where the comments were written in Latin script (non-native script). For both the task, given a comment the participants should develop a system to classify the text into offensive or not-offensive. In total 96 participants participated and 12 participants submitted the papers. In this paper, we present the task, data, the results and discuss the system submission and methods used by participants. © 2020 Copyright for this paper by its authors.
  • No Thumbnail Available
    Item
    Overview of the HASOC-DravidianCodeMix Shared Task on Offensive Language Detection in Tamil and Malayalam
    (CEUR-WS, 2021) Chakravarthi, B.R.; Kumaresan, P.K.; Sakuntharaj, R.; Anand Kumar, M.; Thavareesan, S.; Premjith, B.; Sreelakshmi, K.; Subalalitha, S.C.; Mccrae, J.P.; Mandl, T.
    We present the results of HASOC-Dravidian-CodeMix shared task1 held at FIRE 2021, a track on offensive language identification for Dravidian languages in Code-Mixed Text in this paper. This paper will detail the task, its organisation, and the submitted systems. The identification of offensive language was viewed as a classification task. For this, 16 teams participated in identifying offensive language from Tamil-English code mixed data, 11 teams for Malayalam-English code mixed data and 14 teams for Tamil data. The teams detected offensive language using various machine learning and deep learning classification models. This paper has analysed those benchmark systems to find out how well they accommodate a code-mixed scenario in Dravidian languages, focusing on Tamil and Malayalam. © 2021 Copyright for this paper by its authors.