Performance evaluation of machine learning algorithms for detecting Hindi sarcasm

43-48Sentiments, the common way people express their feelings, have been greatly influenced with the advent of Sarcasm. Being sarcastic is considered trendy and thus people use it extensively in their day-to-day language. Sentiment Analysis, also known as Opinion Mining, has encountered Sarcasm as a challenge since a long time. Sarcasm, which finds few human brains susceptible to its presence and effects, has posed to be the toughest of all problems. One of the issues with Sarcasm Detection is the numerous ways it can be expressed with. Since there has not been a perfect answer to all the Sarcasm issues, this paper attempts to analyse and evaluate the popular Machine learning techniques on mixed sarcasm types

A Model for Translation of Text from Indian Languages to Bharti Braille Characters

People who are visually impaired face a lot of difficulties while studying. One of the major causes to this is lack of available text in Bharti Braille script. In this paper, we have suggested a scheme to convert text in major Indian languages into Bharti Braille. The system uses a hybrid approach where at first the text in Indian language is given to a rule based system and in case if there is any ambiguity then it is resolved by applying a LSTM based model. The developed model has also been tested and found to have produced near accurate results.Comment: ISCON 2023 Conference Paper, 4 Pages, 2 Tables 4 Figures. arXiv admin note: text overlap with arXiv:2305.0615

Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation

In this paper, we have shown the improvement of English to Bharti Braille machine translation system. We have shown how we can improve a baseline NMT model by adding some linguistic knowledge to it. This was done for five language pairs where English sentences were translated into five Indian languages and then subsequently to corresponding Bharti Braille. This has been demonstrated by adding a sub-module for translating multi-word expressions. The approach shows promising results as across language pairs, we could see improvement in the quality of NMT outputs. The least improvement was observed in English-Nepali language pair with 22.08% and the most improvement was observed in the English-Hindi language pair with 23.30%.Comment: 6 Pages, 5 Figures, 2 Table

Implications of Deep Circuits in Improving Quality of Quantum Question Answering

Question Answering (QA) has proved to be an arduous challenge in the area of natural language processing (NLP) and artificial intelligence (AI). Many attempts have been made to develop complete solutions for QA as well as improving significant sub-modules of the QA systems to improve the overall performance through the course of time. Questions are the most important piece of QA, because knowing the question is equivalent to knowing what counts as an answer (Harrah in Philos Sci, 1961 [1]). In this work, we have attempted to understand questions in a better way by using Quantum Machine Learning (QML). The properties of Quantum Computing (QC) have enabled classically intractable data processing. So, in this paper, we have performed question classification on questions from two classes of SelQA (Selection-based Question Answering) dataset using quantum-based classifier algorithms-quantum support vector machine (QSVM) and variational quantum classifier (VQC) from Qiskit (Quantum Information Science toolKIT) for Python. We perform classification with both classifiers in almost similar environments and study the effects of circuit depths while comparing the results of both classifiers. We also use these classification results with our own rule-based QA system and observe significant performance improvement. Hence, this experiment has helped in improving the quality of QA in general.Comment: 26 pages, 7 Figures, 11 Tables, Book Chapter of Book: Quantum Computing: A Shift from Bits to Qubits, Springer, 202