Elizabeth Sherly

TL;DR: The Dravidian-CodeMix-FIRE 2020 Track as discussed by the authors focused on sentiment analysis of code-mixed text in code mixed text for Tamil and Malayalam, and participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognizing whether the comment is not in the intended language.

TL;DR: A set of quality metrics to evaluate the dataset and categorize them accordingly is proposed and will assist users in various natural language processing tasks such as part-of-speech tagging, named entity recognition, sentiment analysis, conversational systems, and machine translation, etc.

TL;DR: In this article, a model of language models for minority and historical languages was developed using a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2 (Insight 2), co-funded by the European Regional Development Fund as well as the EU H2020 programme under grant agreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and IRCLA/2017/129 (CARDAMOM-Comparative Deep Models of Language

TL;DR: This work combines Robust Outlyingness Ratio (ROR) mechanism with extended NL-Means (ROR-NLM) filter based on Discrete Cosine Transform (DCT) for the detection and removal of noise.

TL;DR: A new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators is presented, which obtained a Krippendorff’s alpha above 0.8 for the dataset.