In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

It has been observed from the literature that speech is the most natural means of communication between humans. Human beings start speaking without any tool or any explicit education. The environment surrounding them helps them to learn the art of speaking. From the existing literature, it is found that the existing speaker classification techniques suffer from over-fitting and parameter tuning issues. An efficient tuning of machine learning techniques can improve the classification accuracy of speaker classification. To overcome this issue, in this paper, an efficient particle swarm optimization-based support vector machine is proposed. The proposed and the competitive speaker classification techniques are tested on the speaker classification data of Punjabi persons. The comparative analysis of the proposed technique reveals that it outperforms existing techniques in terms of accuracy, [Formula: see text]-measure, specificity and sensitivity.

Sign Language (SL), also known as gesture-based language, is used by people with hearing loss to convey their messages. SL interpreters are required for people who do not have the knowledge of SL, but interpreters are not readily available. Thus, a machine-based translation system is required to translate the text into SL. In this article, a system is implemented for translating English text into Indian Sign Language (ISL). It acts as a tool for human-computer interaction and eliminates the need for an ISL human interpreter for communicating with people who have hearing loss. The system features a rich corpus of English words and commonly used sentences. It consists of components such as an ISL parser, the Hamburg Notation System, the Signing Gesture Mark-up Language, and 3D avatar animation for generating SL according to ISL grammar. The proposed system has been tested rigorously by SL users. The results proved that the proposed system is highly efficient and achieves an average score of accuracy (i.e., 4.2 for English words and 3.8 for sentences on a scale from 1 to 5). The performance of proposed system has also been evaluated using the BiLingual Evaluation Understudy score, which results in 0.95 accuracy. The proposed system and mobile application together has the potential to bring individuals with hearing loss and their entourage together.

WordNet is an electronic lexical database available on-line as a powerful resource to the researchers in the area of computational linguistics, text processing and other related areas.WordNet for Hindi language has already been developed by IIT, Bombay.The Indian languages WordNets are being created using expansion approach from Hindi Word-Net under IndoWordNet project.In expansion approach, semantic relations are borrowed from the reference language, while the lexical relations need to be created for each language, as these relations are language dependent.This paper describes the process of creation of lexical relations like antonym, compounding, conjunction and gradation for IndoWordNet.A lexical creation tool has been presented in this paper with provision to create lexical relations in target language on the basis of relations created in Hindi Word-Net and with another provision to create lexical relations in target language without referring to Hindi WordNet.It has been observed that lexical relations for target language can be created easily on the basis of relations created in Hindi WordNet for Hindi in-family languages, while for the languages that do not fall in the same family provision of creation of lexical relation without referring to Hindi WordNet can be used.

DeConverter is core software in a Universal Networking Language (UNL) system. A UNL system has EnConverter and DeConverter as its two major components. EnConverter is used to convert a natural language sentence into an equivalent UNL expression, and DeConverter is used to generate a natural language sentence from an input UNL expression. This paper presents design and development of a Punjabi DeConverter. It describes five phases of the proposed Punjabi DeConverter, i.e., UNL parser, lexeme selection, morphology generation, function word insertion, and syntactic linearization. This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter. Syntactic linearization is the process of defining arrangements of words in generated output. The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph, a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper. Special cases of syntactic linearization with respect to Punjabi language for UNL relations like ‘and’, ‘or’, ‘fmt’, ‘cnt’, and ‘seq’ have also been presented in this paper. This paper also provides implementation results of the proposed Punjabi DeConverter. The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology (IIT), Bombay, India, as gold-standards. The proposed system generates 89.0% grammatically correct sentences, 92.0% faithful sentences to the original sentences, and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale. The system is also able to achieve a bilingual evaluation understudy (BLEU) score of 0.72.