This paper investigates the sentiment analysis of the” scholarship system” 4, in Odisha, primarily, to identify why some students do not apply for government-sponsored scholarships. Our research ...focuses on social media platforms, surveys, and machine learning-based analyses to understand the decision-making process and increase awareness about the various scholarship schemes. The goal of our experiment is to determine the efficacy of sentiment analysis in evaluating the effectiveness of different scholarship schemes. A wide variety of techniques based on dictionaries; corpora lexicons are used in different scholarship schemes for sentiment analysis. Our research paper is based on an evaluation process that could have a positive effect on the government by improving scholarship programs and giving financial aid to students from poor families, which would raise the level of education in Odisha. Our research paper concludes with a summary of successful and unsuccessful schemes, as well as their Word frequency counts and Sentiment Polarity scores.
The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concept’s semantics. As a result, IC has ...been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.
E-learning is inevitable during these pandemic days and most of the learners find it comfortable to learn online. However, the main challenge is to locate the appropriate data in line with the ...learner's requirement. Considering the necessity of this issue, this article presents an e-content recommendation system that considers the user's learning capability. This work categorizes the documents into three categories such as basic, intermediate and advanced levels. Based on the users' learning capability, corresponding documents are recommended and this idea enhances the overall learning experience. This work is based on three phases such as data pre-processing, feature extraction and classification. The collected documents are pre-processed for preparing the documents suitable for further processes. Features such as Parts–OF–Speech (POS) tagging, Term Frequency - Inverse Document Frequency (TF-IDF) and semantic similarity based on WordNet are extracted and the multiclass Support Vector Machine (SVM) is employed for distinguishing between the classes. The performance of the work is tested and the results prove the efficacy of the work with 98 % accuracy rates, in contrast to the comparative techniques.
The efficiency of the Honeywords approach has been proven to be a significant tool for boosting password security. The suggested system utilizes the Meerkat Clan Algorithm (MCA) in conjunction with ...WordNet to produce honeywords, thereby enhancing the level of password security. The technique of generating honeywords involves data sources from WordNet, which contributes to the improvement of authenticity and diversity in the honeywords. The method encompasses a series of consecutive stages, which include the tokenization of passwords, the formation of alphabet tokens using the Meerkat Clan Algorithm (MCA), the handling of digit tokens, the creation of unique character tokens, and the consolidation of honeywords. The optimization of the performance of the Meerkat Clan Algorithm (MCA) involves the careful selection of parameters. The experimental findings have exhibited noteworthy levels of precision and optimum efficacy, particularly in tasks such as proposing words with similar meanings, forecasting numerical values, and producing distinctive symbols. The attainment of this achievement is facilitated by a confluence of factors, encompassing the caliber of data, the judicious use of algorithms or models, and the ongoing process of iterative improvement to consistently enhance outcomes. In order to achieve the appropriate levels of accuracy and functionality, it is crucial to engage in the process of conducting experiments, thoroughly testing the system, and making necessary improvements. The empirical findings provide confirmation of the effectiveness of the MCA in producing a varied and protected collection of honeywords. This is especially evident in the case of alphabet tokens, which are distinguished by their autonomous creation and strong security characteristics. The analysis of correction rates, specifically in relation to the password "Lion1999*," demonstrates the aforementioned results. This study reveals an average accuracy of honeyword production up to 0.729847632111541. In the same manner, the accuracy of the password "house2000" is determined to be 0.761325846711256. Additionally, when considering a sample of 100 passwords, the mean accuracy of honeyword creation is calculated to be 0.7073897168887518. The findings collectively highlight the effectiveness of the MCA in generating honeywords that possess improved security characteristics.
Various applications in computational linguistics and artificial intelligence rely on high-performing word sense disambiguation techniques to solve challenging tasks such as information retrieval, ...machine translation, question answering, and document clustering. While text comprehension is intuitive for humans, machines face tremendous challenges in processing and interpreting a human’s natural language. This paper presents a novel knowledge-based word sense disambiguation algorithm, namely Sequential Contextual Similarity Matrix Multiplication (SCSMM). The SCSMM algorithm combines semantic similarity, heuristic knowledge, and document context to respectively exploit the merits of local sense-based context between consecutive terms, human knowledge about terms, and a document’s main topic in disambiguating terms. Unlike other algorithms, the SCSMM algorithm guarantees the capture of the maximum sentence context while maintaining the terms’ order within the sentence. The proposed algorithm outperformed all other algorithms when disambiguating nouns on the combined gold standard datasets, while demonstrating comparable results to current state-of-the-art word sense disambiguation systems when dealing with each dataset separately. Furthermore, the paper discusses the impact of granularity level, ambiguity rate, sentence size, and part of speech distribution on the performance of the proposed algorithm.
•Semantic similarity affects the overall performance of knowledge-based Word Sense Disambiguation (WSD) systems.•With Semantic similarity, sense heuristics, and document context, we designed a novel knowledge-based word sense disambiguation algorithm.•The Sequential Contextual Similarity Matrix Multiplication (SCSMM) algorithm captures the maximum sentence context while maintaining the words’ order.•The SCSMM algorithm outperforms current WSD systems when disambiguating nouns.
The Romanian WordNet (RoWN) database is an impressive collection of Romanian nouns, verbs, adjectives, and adverbs, which can be seen as a network of nodes where, words can be found in a certain ...context to have the same meaning, and in the mesh of that network are found the other words that in turn can become a network node for another word in a specific context. In this study we propose an approach for the problem of aligning the word senses form RoWordNet with those of the Thesaurus Dictionary of the Romanian Language in electronic format (eDTLR), by exploiting the collections of definitions and examples in the two linguistic thesauri, based on a statistical model, where four heuristics are proposed for solving this issue.
Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorisation, memory, decision-making and reasoning. For this reason, the proposal of ...methods for the estimation of the degree of similarity and relatedness between words and concepts has been a very active line of research in the fields of artificial intelligence, information retrieval and natural language processing among others. Main approaches proposed in the literature can be categorised in two large families as follows: (1) Ontology-based semantic similarity Measures (OM) and (2) distributional measures whose most recent and successful methods are based on Word Embedding (WE) models. However, the lack of a deep analysis of both families of methods slows down the advance of this line of research and its applications. This work introduces the largest, reproducible and detailed experimental survey of OM measures and WE models reported in the literature which is based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem. We show that WE models which combine distributional and ontology-based information get the best results, and in addition, we show for the first time that a simple average of two best performing WE models with other ontology-based measures or WE models is able to improve the state of the art by a large margin. In addition, we provide a very detailed reproducibility protocol together with a collection of software tools and datasets as supplementary material to allow the exact replication of our results.
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•A large reproducible survey of ontology-based similarity measures and word embeddings.•Embeddings using ontologies get the best overall results on word similarity and relatedness.•Best performing WordNet-based similarity measures use IC models & path-based features.•Linear combinations of best-performing word embeddings improve the state of the art.
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.
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