ABSTRACT
Analyzing sentiments or opinions in code-mixed languages is gaining importance due to increase in the use of social media and online platforms especially during the Covid-19 pandemic. In a multilingual society like India, code-mixing and script mixing is quite common as people especially the younger generation are quite familiar in using more than one language. In view of this, the current paper describes the models submitted by our team MUCIC for the shared task in’Sentiments Analysis (SA) for Dravidian Languages in Code-Mixed Text’. The objective of this shared task is to develop and evaluate models for code-mixed datasets in three Dravidian languages, namely: Kannada, Malayalam, and Tamil mixed with English language resulting in Kannada-English (Ka-En), Malayalam-English (Ma-En), and Tamil-English (Ta-En) language pairs. N-grams of char, char sequences, and syllables features are transformed into feature vectors and are used to train three Machine Learning (ML) classifiers with majority voting. The predictions on the Test set obtained average weighted F1-scores of 0.628, 0.726, and 0.619 securing 2nd, 4th, and 5th ranks for Ka-En, Ma-En, and Ta-En language pairs respectively. © 2021 Copyright for this paper by its authors.
ABSTRACT
Kerala, a South Indian state has tribal population in all her districts. About 1.5% of the total population of the state constitute tribal population. They depend upon natural environment and resources for their survival. Children from the same community usually depend on government funded schools for their education. Education for this deprived section during COVID 19 Pandemic was a massive exclusion and an uphill task. Digital divide and medium of communication (Standard Malayalam) were some of the critical concerns to knowledge acquisition among tribal children. This paper primarily focuses on the challenges of online education among tribal students with a clear emphasis on the English language acquisition. This study was conducted in four most tribal populated districts of the State, namely, Wayanad, Malappuram, Palakkad, and Idukki. This is a qualitative explorative study that explores the experiences of the tribal students' English language learning challenges from the teachers' perspective in these districts. © The Electrochemical Society
ABSTRACT
Covid-19 is a global pandemic, has affected millions of people physically and mentally. The dynamic and rapidly growing situation with COVID-19 made it more difficult to discourse accurate and authoritative information about the disease, in most of the Indian local languages like Malayalam. To resolve this issue, here we propose a semantic Malayalam Dialogue System for COVID-19 related Question Answering. This is a user-friendly knowledge system to automatically deliver relevant answers to COVID-19 related queries in the Malayalam language. The proposed system proceeds in three stages;Document pre-processing, Semantic modelling using word embedding and Answer Retrieval. The NLP techniques are used for document processing, word embedding - CBOW and Skip Gram methods, Neural Network models are used for Semantic Modelling and finally, a cosine similarity measure is used to map and retrieve the answers for the user's queries. The experiment was conducted with our own Malayalam dataset;and compared the performance of two Word2Vec algorithms - CBOW and Skip Gram. The result, with our data set, shows that Skip-Gram is more efficient than CBOW and CBOW is faster than the Skip Gram model. © 2021 IEEE.