Internet users perceive a multilingual web but are unfamiliar with it due to communication in their regional language called Cross-Lingual Information Retrieval (CLIR). In CLIR, a translation technique is used to translate the user queries into the target document’s language. Conventional translation techniques are based on either a manual dictionary or a parallel corpus, whereas the trending Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) techniques are trained on a parallel corpus. NMT is not so mature for Hindi-English translation, according to the literature, and SMT performs better than the NMT. SMT provides a static translation due to the limited vocabularies in the available parallel corpus. It may not provide the translations for missing or unseen words, whereas the web provides a dynamic interface where multiple users are updating information at the same time. The web may provide the translations for missing or unseen words, and therefore the web is effectively used for technically developed languages like English, German, Spanish, Russian, and Chinese. In this article, different web resources such as Wikipedia, Hindi WordNet and Indo WordNet, ConceptNet, and online dictionary based translation techniques are proposed and applied to Hindi-English CLIR. Wikipedia-based translation approach incorporates three modules—exactly matched, partially matched, and disambiguation—to address the issues of wrong inter-wiki links, partially matched terms, and ambiguous articles. Hindi WordNet and Indo WorNet attribute “English synset” and ConceptNet attributes “Related term” & “Synonymy” are used for obtaining translations. Further, WordNet path similarity is used to disambiguate translations. Various online dictionaries are available that return multiple relevant and irrelevant translations. The proposed approaches are compared to the SMT where the Wikipedia-based approach achieves approximately similar mean average precision to SMT.

Abstractive Text Summarization (ATS) is a task to create a novel summary by generating fresh sentences incorporating new words or rephrasing the article. It is a complex task as the model needs to understand the semantic similarity between the sentences of the text. To fulfill this, there is a need for a large annotated benchmark dataset, which is available for resource-rich languages such as English and non-indic languages. In contrast, for the less-resourced languages, such as Indic languages, the available datasets are limited and involve very short summary sentences. Hence, a language-specific abstractive summarization dataset called HindiSumm was introduced for Hindi, consisting of 570,000 text-summary pairs from Navbharat Times across 21 domains. The HindiSumm dataset’s efficiency is evaluated extrinsically and intrinsically by using various metrics. Furthermore, two recent multilingual-cased pre-trained models are fine-tuned on the HindiSumm dataset individually. In addition, an ensembled approach using weighted averaging is also incorporated to check the efficacy of the proposed dataset. The model is tested with the in-house created dataset, and results are evaluated on ROUGE scores and show significant improvements of around 13.2% for the proposed HindiSumm compared with other benchmark datasets. In the future, the HindiSumm dataset will promote the progress of ATS for the Indian language.

Natural Language Processing (NLP) has been in practice for the past couple of decades, and extensive work has been done for the Western languages, particularly the English language. The Eastern counterpart, especially the languages of the Indian subcontinent, needs attention as not much language processing work has been done on these languages. Western languages are rich in dictionaries, WordNet, and associated tools, while Indian languages are lagging behind in this segment. Marathi is the third most spoken language in India and the 15th most spoken language worldwide. Lack of resources, complex linguistic facts, and the inclusion of prevalent dialects of neighbors have resulted in limited work for Marathi. The aim of this study is to provide an insight into the various linguistic resources, tools, and state-of-the-art techniques applied to the processing of the Marathi language. Initially, morphological descriptions of the Marathi language are provided, followed by a discussion on the characteristics of the Marathi language. Thereafter, for Marathi language, the availability of corpus, tools, and techniques to be used to develop NLP tasks is reviewed. Finally, gap analysis is discussed in current research and future directions for this new and dynamic area of research are listed that will benefit the Marathi Language Processing research community.

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.

Sentiments are feelings, emotions likes and dislikes or opinions which can be articulate through text, images or videos. Sentiment Analysis on web data is now becoming a budding research area of social analytics. Users express their sentiments on the web by exchanging texts and uploading images through a variety of social media like Instagram, Facebook, Twitter, WhatsApp etc. A lot of research work has been done for sentiment analysis of textual data; there has been limited work that focuses on analyzing the sentiment of image data. Image sentiment concepts are ANPs i.e. Adjective Noun Pairs automatically discovered tags of web images which are useful for detecting the emotions or sentiments conveyed by the image. The major challenge is to predict or identify the sentiments of unlabelled images. To overcome this challenge deep learning techniques are used for sentiment analysis, as deep learning models have the capability for effectively learning the image behavior or polarity. Image recognition, image prediction, image sentiment analysis, and image classification are some of the fields where Neural Network (NN) has performed well implying significant performance of deep learning in image sentiment analysis. This paper focuses on some of the noteworthy models of deep learning as Deep Neural Network (DNN), Convolutional Neural Network (CNN), Region-based CNN (R-CNN) and Fast R-CNN along with the suitability of their applications in image sentiment analysis and their limitations. The study also discusses the challenges and perspectives of this rising field.

Cerebral palsy (CP) is a neuro-development disease in children. It is quite an intricate task to categorize gait pattern into normal and CP based pathology. In this study, nature-inspired meta-heuristic algorithms are explored on a publicly available gait dataset of 156 subjects for automatic gait profiling of children with cerebral palsy. Five cases are considered to explore the feature selection criteria before applying clustering technique. Finding the optimal number of clusters is a challenging task in the unsupervised learning area. In this study, an optimal number of gait profiles in the datasets is identified based on voting from mean square error, silhouette coefficient and Dunn index. The study demonstrates that optimized based gait profile clusters could assist quantitatively in clinical rehabilitation evaluation for the children affected by CP.

In India, cervical cancer is the second most common type of cancer in females. Pap smear is a simple cytology test for the detection of cancer in its early stages. To obtain the best results from the Pap smear, expert pathologist are required. Availability of pathologist in India is far below the required numbers, especially in rural parts. In this paper, multiple texture‐based features are introduced for the extraction of relevant and informative features from single‐cell images. First‐order histogram, GLCM, LBP, Laws, and DWT are used for texture feature extraction. These methods help to recognise the contour of the nucleus and cytoplasm. ANN and SVM are used to classify the single‐cell images either normal or cancerous based on the trained features. ANN and SVM are used on every single feature as well as on the combination of all features. Best results are obtained with a combination of all features. The system is evaluated on generated dataset MNITJ, containing 330 single cervical cell images and also on publicly available benchmark Herlev data set. Experimental results show that the proposed texture‐based features give significantly better results in cervical cancer detection when compared with state of the art shape‐based features regarding accuracy.

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

This paper proposes a hybrid approach of personalized Web Information Retrieval that utilizes (1) ontology for retrieval of user's context (2) user profile that is temporarily updated according to users' browsing behavior and (3) collaborative filtering for considering recommendation of similar users. Empirical analysis reveals that Precision, Recall and F-Score of most of the queries for many users are improved with using the proposed method.