An Ensemble Method to Classify Telugu Idiomatic Sentences using Deep Learning Models

Natural Language Processing (NLP) involves the use of algorithms and models and various computational techniques to analyze, process, and generate natural language data, including speech and text. NLP helps computers interact with humans in a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze voluminous unstructured data, including social media posts, newspaper articles, reviews from customers, emails, and others. It helps organizations extract insights, automate tasks, and improve decision-making by enabling machines to understand and generate human-like language. A linguistic background is essential for understanding NLP. Linguistic theories and models help in developing NLU systems, as NLP specialists need to understand the structure and rules of language. NLU systems are organized into different components, including language modelling, parsing, and semantic analysis. NLU systems may be assessed through the use of metrics that includes measures like precision and recall, as well as indicators that convey meaningful information that include F1 score and others. Semantics and knowledge representation are central to NLU, as they involve understanding the meaning of words and sentences and representing this information in a way that machines can use. Approaches to knowledge representation include semantic networks, ontologies, and vector embeddings. Language modelling is an essential step in NLP that sees usage in applications like speech recognition, text generation, and text completion and also in areas such as machine translation. Ambiguity Resolution remains a major challenge in NLP, as language is often ambiguous and context-dependent. Some common applications of NLP include sentiment analysis, chatbots, virtual assistants, machine translation, speech recognition, text classification, text summarization, and information extraction. In this chapter, we show the applicability of a popular unsupervised learning technique, viz., clustering through K-Means. The efficiency provided by the K-Means algorithm can be improved through the use of an optimization loop. The prospects for NLP are promising, with an increasing demand for AI-powered language technologies in various industries, including healthcare, finance, and e-commerce. There is also a growing need for ethical and responsible AI systems that are transparent and accountable.

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Abstract: This chapter explores the cutting-edge advancements in computational language technology, delving into the transformative impact of recent innovations in the field. It covers the evolution from traditional rule-based and statistical methods to advanced neural network architectures, such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers. The chapter highlights key applications in natural language processing (NLP), including speech recognition, machine translation, and sentiment analysis, while addressing the ethical and societal implications of these technologies. Emphasis is placed on emerging research areas, such as multilingual NLP, low-resource language processing, and conversational AI. The chapter concludes by discussing future trends and challenges, including scalability, interpretability, and fairness in NLP systems, providing a comprehensive overview of the current state and future directions of computational linguistics. Keywords: Computational Linguistics,Natural Language Processing (NLP),Neural Networks,Deep Learning,Speech Recognition,Machine Translation,Sentiment Analysis,Multilingual NLP,Low-Resource Languages,Conversational AI,Ethical AI,Data Privacy,Algorithmic Bias and Future Trends in NLP.

With the recent advances in deep learning, different approaches to improving pre-trained language models (PLMs) have been proposed. PLMs have advanced state-of-the-art (SOTA) performance on various natural language processing (NLP) tasks such as machine translation, text classification, question answering, text summarization, information retrieval, recommendation systems, named entity recognition, etc. In this paper, we provide a comprehensive review of prior embedding models as well as current breakthroughs in the field of PLMs. Then, we analyse and contrast the various models and provide an analysis of the way they have been built (number of parameters, compression techniques, etc.). Finally, we discuss the major issues and future directions for each of the main points.

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from an artificial intelligence perspective. In several domains, the importance of natural language processing is paramount and ever growing, as digital information in the form of language is ubiquitous. Applications of natural language processing include language translation, sentiment analysis, web search applications, customer service automation, text classification, topic detection from text, language modeling, and so forth. Traditional methods of natural language processing relied on the bag of word models, the vector space of word model, and on-hand coded knowledge bases and ontologies. One of the key areas for natural language processing is the syntactic and semantic analysis of language. Syntactic analysis refers to how words are grouped and connected in a sentence. The main tasks in syntactic analysis are tagging parts of speech, detecting syntactic classes (such as verbs, nouns, noun phrases, etc.), and assembling sentences by constructing syntax trees. Semantic analysis refers to complex tasks such as finding synonyms, performing word-verb disambiguation, and so on. As part of this chapter, we will begin with a brief introduction of the traditional natural language processing techniques and then move on to deep learning-based methods involving recurrent neural networks, attention, and transformers.

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages.

• CRFLV-I and CRFLV-II are proposed for sequence labeling. • Encoding schema is administered in the form of latent variables able to capture structures of hidden variables. • The proposed models make use of different schemas in different scenarios. • We show that our schemas holds strong performance against BIO or BILOU encoding schemas. To process data like text and speech, Natural Language Processing (NLP) is a valuable tool. As on of NLP’s upstream tasks, sequence labeling is a vital part of NLP through techniques like text classification, machine translation, and sentiment analysis. In this paper, our focus is on sequence labeling where we assign semantic labels within input sequences. We present two novel frameworks, namely SA-CRFLV-I and SA-CRFLV-II, that use latent variables within random fields. These frameworks make use of an encoding schema in the form of a latent variable to be able to capture the latent structure in the observed data. SA-CRFLV-I shows the best performance at the sentence level whereas SA-CRFLV-II works best at the word level. In our in-depth experimental results, we compare our frameworks with 4 well-known sequence prediction methodologies which include NER, reference parsing, chunking as well as POS tagging. The proposed frameworks are shown to have better performance in terms of many well-known metrics.

Natural language processing (NLP) has been widely applied in machine translation, speech recognition and other fields. Many elegant results have been proposed such as text classification, sentiment analysis. In recent years, the Bert model has made a great breakthrough in many NLP tasks. Afterwards, using transformer as a feature extractor has been achieving good performance in many scenarios. However, there are too many model parameters in the Bert model. In order to solve this problem, we propose an improved algorithm for the Bert model, which shares the first 11 layers' parameters. The proposed method could largely reduce the number of parameters in the model. We also conduct experiments to evaluate the algorithm's performance; the results also validate that the improved algorithm for the Bert model could achieve good results.

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

This review article highlights the paradigm shift brought in by LLMS in NLP, coupling their capabilities and applications with related challenges. Architectures like Transformers have given LLMs prominence. GPT, BERT, and PaLM brought tremendous improvement over tasks like text classification, machine translation, and conversational AI, demonstrating almost human-level accuracy in comprehension and generation. These models pave the way for applications in healthcare, finance, and multilingual customer service-disconnected domains made possible by their generalization and transfer learning capabilities. With the Adoption of LLMs, there is also a growing concern for high computational costs, data privacy, and inherent biases, which pose an ethical and environmental challenge. The paper concludes with a gaze into the future of NLP, emphasizing that responsible development practices should be followed to maximize the benefits of LLMs while decreasing their societal and environmental impact.

This Chapter discusses the application of Natural Language Processing (NLP) in the legal domain for entity identification and the generation of hierarchy-based applications. NLP techniques, such as Named Entity Recognition (NER) and information extraction, are utilized to analyse legal documents, extract key information, and understand contextual relationships. The integration of NLP facilitates tasks such as legal document summarization, contract analysis, automated document generation, and legal research, thereby enhancing the efficiency and accuracy of legal processes. The creation of hierarchical structures within documents, language translation, sentiment analysis, and compliance monitoring further contribute to the comprehensive utilization of NLP in the legal field. The abstract highlights the transformative impact of NLP in streamlining legal workflows, improving information retrieval, and ensuring compliance with evolving legal standards. Most judicial systems in the world, centered around the Supreme Court as the highest-level court, grapples with a substantial caseload, receiving over 7,500 cases annually but hearing fewer than 150. The Supreme Court's unique role in interpreting the constitutionality of laws and establishing precedents underscores its significant influence. Challenges arise from the overwhelming caseload, leading to increased use of plea bargains and potential concerns about justice. To address these issues, a proposed research initiative suggests employing machine learning and natural language processing to accelerate Supreme Court decisions, aiming to uncover pivotal aspects that greatly influence the outcomes, and enhance our comprehension on India legal system's functioning and constraints. This chapter presents a methodology for sentiment-based summarization of legal documents using Natural Language Processing (NLP) techniques. The process involves pre-processing the text, conducting sentiment analysis to label sentences as positive, negative, or neutral, and then summarizing the document while incorporating the identified sentiments. Special attention is paid to legal-specific considerations such as terminology and context.

Discourse analysis plays a significant role in understanding and interpreting the complexity of human language, providing valuable insights for improving natural language processing (NLP) systems. This study presents an in-depth review of discourse analysis in NLP, and its applications in various domains such as machine translation, question-answering systems, sentiment analysis, conversational bots, and information retrieval. By analyzing the structural and contextual aspects of language, discourse analysis enhances the NLP system’s ability to understand individual words and their meaning in cross-border conversational contexts. The review highlights how discourse analysis and learning techniques work together to improve the automated system's accuracy and efficiency. In addition to addressing significant challenges, including ambiguity, cultural variance, and conversational dynamics, the paper also suggests future research directions for exploring the possibilities of natural language processing in human-computer interactions. This work attempts to fill the gap between linguistic theory and computational applications by demonstrating how discourse analysis can revolutionize the development of more intelligent, contextually aware, and human-centered language technologies.

Natural Language Processing (NLP) has emerged as a transformative tool for analyzing and understanding human languages, including low-resource languages like Afan Oromo. This paper explores the application of NLP techniques for Afan Oromo text analysis, focusing on challenges such as limited linguistic resources, morphological complexity, and the need for robust computational models. Key techniques discussed include tokenization, stemming, part-of-speech tagging, named entity recognition (NER), and sentiment analysis, tailored to the unique grammatical and syntactic structures of Afan Oromo. The study highlights the importance of developing annotated corpora, leveraging machine learning models, and integrating transfer learning to improve accuracy and efficiency. The findings underscore the potential of NLP to preserve and promote Afan Oromo in digital spaces, enabling applications in machine translation, information retrieval, and educational tools. Future research directions include expanding datasets, enhancing model performance, and addressing dialectal variations within the language.

In recent years, computer systems are widely used in the modern Chinese part of speech tagging. Modern Chinese part of speech tagging is a basic subject in the natural language processing. It is widely used in machine translation, natural language understanding, establishing of the Chinese corpus, information retrieval, text classification, text proofreading and speech recognition, among others. In the part of speech tagging, multi-category words part of speech (POS) tagging is always a difficulty. Although the total number of multi-category words in the modern Chinese is not high, the usage is fairly widespread. This paper, proposes an algorithm of multi-category words part of speech tagging. First, it is word segmentation according to the traditional method. And then, on this basis, we introduce a method based on the rules of multi-category words part of speech tagging. Finally, a detailed description of the Hidden Markov Model (HMM) used in the words part of speech tagging, and a statistical algorithm based on Hidden Markov Model. KeywordsChinese information processing; Multi-category words; Part of speech tagging;