Multi-level mining and visualization of scientific text collections

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.

Abstractive single document summarization is considered as a challenging problem in the field of artificial intelligence and natural language processing. Meanwhile and specifically in the last two years, several deep learning summarization approaches were proposed that once again attracted the attention of researchers to this field.It is a well-known issue that deep learning approaches do not work well with small amounts of data. With some exceptions, this is, unfortunately, the case for most of the datasets available for the summarization task. Besides this problem, it should be considered that phonetic, morphological, semantic and syntactic features of the language are constantly changing over the time and unfortunately most of the summarization corpora are constructed from old resources. Another problem is the language of the corpora. Not only in the summarization field, but also in other fields of natural language processing, most of the corpora are only available in English. In addition to the above problems, license terms, and fees of the corpora are obstacles that prevent many academics and specifically non-academics from accessing these data.This work describes an open source framework to create an extendable multilingual corpus for abstractive single document summarization that addresses the above-mentioned problems. We describe a tool consisted of a scalable crawler and a centralized key-value store database to construct a corpus of an arbitrary size using a news aggregator service.

Abstract. Large language models (LLMs) have revolutionized the field of natural language processing (NLP), demonstrating remarkable capabilities in understanding, generating, and manipulating human language. This comprehensive review explores the development, applications, optimizations, and challenges of LLMs. This paper begin by tracing the evolution of these models and their foundational architectures, such as the Transformer, GPT, and BERT. We then delve into the applications of LLMs in natural language understanding tasks, including sentiment analysis, named entity recognition, question answering, and text summarization, highlighting real-world use cases. Next, we examine the role of LLMs in natural language generation, covering areas such as content creation, language translation, personalized recommendations, and automated responses. We further discuss LLM applications in other NLP tasks like text style transfer, text correction, and language model pre-training. Subsequently, we explore techniques for optimizing and improving LLMs, including model compression, explainability, robustness, and security. Finally, we address the challenges posed by the significant computational requirements, sample inefficiency, and ethical considerations surrounding LLMs. We conclude by discussing potential future research directions, such as efficient architectures, few-shot learning, bias mitigation, and privacy-preserving techniques, which will shape the ongoing development and responsible deployment of LLMs in NLP.

Language technologies applied to document simplification for helping autistic people

The field of natural language processing (NLP) is being increasingly applied to the realm of healthcare. This abstract delves into the intersection of health care and NLP, highlighting how this technology is being used to enhance various aspects of the healthcare industry. From analyzing medical records and extracting relevant information to aiding in diagnosis and treatment recommendations, NLP is playing a pivotal role in transforming how healthcare professionals interact with data and make informed decisions. This abstract provides an overview of the key applications and benefits of employing NLP in healthcare, showcasing its potential to improve patient outcomes and streamline medical processes. This research seeks to harness the power of advanced computational linguistics to revolutionize various aspects of the healthcare industry. By enabling computers to understand and interpret human language, NLP has the potential to drive transformative changes NLP-powered systems can analyze vast amounts of medical literature, patient records, and research findings to provide healthcare practitioners with timely and relevant information. This assists in making well-informed decisions regarding diagnoses, treatment plans, and prognoses. NLP techniques facilitate the extraction of pertinent information from unstructured medical records, including patient histories and doctor's notes. This structured data can then be used for research, trend analysis, and epidemiological studies, contributing to evidence-based medicine. Versatile with unique alternatives a new method for optimization is proposed MOORA. This method is objective denotes the matrix of responses of the alternatives, however, proposing better policies, which rates are used. Well established. Multi-objective another method for optimization is used for comparison, reference point method. Then, various competitions this proved to be the best choice among the methods. From the result BC2GM is in 1st rank whereas BC5- Disease is in lowest rank.

Manual Summarization of large bodies of text involves a lot of human effort and time, especially in the legal domain. Lawyers spend a lot of time preparing legal briefs of their clients' case files. Automatic Text summarization is a constantly evolving field of Natural Language Processing(NLP), which is a subdiscipline of the Artificial Intelligence Field. In this paper a hybrid method for automatic text summarization of legal cases using k-means clustering technique and tf-idf(term frequency-inverse document frequency) word vectorizer is proposed. The summary generated by the proposed method is compared using ROGUE evaluation parameters with the case summary as prepared by the lawyer for appeal in court. Further, suggestions for improving the proposed method are also presented.

This paper introduces the Morphologically-Analyzed and Syntactically-Annotated Quran (MASAQ) dataset, a comprehensive resource designed to address the scarcity of annotated Quranic Arabic corpora and facilitate the development of advanced Natural Language Processing (NLP) models. The Quran, being a cornerstone of classical Arabic, presents unique challenges for NLP due to its sacred nature and complex linguistic features. MASAQ provides a detailed syntactic and morphological annotation of the entire Quranic text, utilizing a rigorously verified text from Tanzil.net. The dataset includes more than 131K morphological entries and 123K instances of syntactic functions, covering a wide range of grammatical roles and relationships. The annotation process involved a team of expert Arabic linguists who employed traditional i'rab methodologies to ensure high accuracy and consistency. The dataset is structured in multiple formats (tab-separated text file (tsv), SQLite3 database (.db), comma-separated file (csv), and JavaScript Object Notation (.JSON)) to cater to various research needs. MASAQ's unique features include a comprehensive tagset of 72 syntactic roles, detailed morphological analysis, and context-specific annotations. This dataset is particularly valuable for tasks such as dependency parsing, grammar checking, machine translation, and text summarization. The potential applications of MASAQ are vast, ranging from pedagogical uses in teaching Arabic grammar to developing sophisticated NLP tools. By providing a high-quality, syntactically annotated dataset, MASAQ aims to advance the field of Arabic NLP, enabling more accurate and more efficient language processing tools. The dataset is made available under the Creative Commons Attribution 3.0 License, which governs its use and distribution. It has been created in compliance with ethical guidelines and with respect for the integrity of the Quranic text.

During the past decade there have been significant advances in the field of Natural Language Processing (NLP) and, in particular, Information Extraction (IE) which have fueled the development and deployment of a wide variety of systems in diverse application areas. However, one application area that has only recently been explored by the NLP community is the area of Information Assurance and Security. In this paper we describe a system for human authentication called QDP, or Query-Directed Passwords, that uses information extraction techniques with the World Wide Web as a database to provide feedback, from the security viewpoint, on the quality of a hint and answer pair input by a user.

Text summarization is a challenging task in the field of Natural Language Processing. In the case of lower resource language like Bengali is also a difficult task to make an automatic text summarization system. This paper is based on the extractive summarization of Bengali text. Text summarization deletes the less useful information in a piece of text or a paragraph and summarizes it into a confined text. This helps in finding the required text more effectively and quickly. There are many types of algorithms used for summarizing the text. Here in this paper, we have used TF-IDF and BERT-SUM technologies for the Bengali extractive text summarization.

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.

Large Language Models (LLMs) such as GPT, LLaMA, and PaLM have transformed the field of Natural Language Processing (NLP) by achieving remarkable results in text generation, summarization, translation, question answering, and dialogue systems. Their wide adoption across industries highlights their usefulness but also exposes a critical limitation—hallucination. Hallucination occurs when models generate information that is false, misleading, or fabricated. These errors can vary from small factual mistakes, like incorrect dates or figures, to serious inaccuracies that may cause harm in sensitive areas such as healthcare, education, and software development. This paper explores the concept and classification of hallucinations in LLMs, examines techniques to reduce them—including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG)—and discusses ethical implications and real-world applications. By comparing multiple strategies, the study aims to contribute to developing more reliable and trustworthy AI systems.

In the past half century, automatic summarization has been a hot topic in the field of natural language processing, and it will be paid more and more attention to with the rapid development of the mobile network technology. Most of the automatic summarization research today is on extractive summarization, which mainly ranks the sentences according to their simple heuristic features such as the frequency of words they contain, their position in the text or paragraph and so on. Inspired by the great performance of LexRank, we manage to introduce LexRank to Chinese texts. In order to make up the deficiency of LexRank, spectral clustering is adopted to process the component analysis. All in all, we propose an approach of extractive summarization for Chinese text based on the combination of spectral clustering and LexRank, which is of high coverage and low redundancy. It is demonstrated by experiments that our approach has been greatly improved compared to the original LexRank. In addition, our approach is easy to implement and robust to noise.

The burgeoning field of natural language processing (NLP) has witnessed exponential growth, captivating researchers due to its diverse practical applications across industries. However, the intricate nature of legal texts poses unique challenges for conventional text extraction methods. To surmount these challenges, this article introduces a pioneering legal text extraction model rooted in fuzzy language processing and metaphor recognition, tailored for the domain of online environment governance. Central to this model is the utilization of a bidirectional long short-term memory (Bi-LSTM) network, adept at delineating illicit behaviors by establishing connections between legal provisions and judgments. Additionally, a self-attention module is integrated into the Bi-LSTM architecture, augmented by L2 regularization, to facilitate the efficient extraction of legal text information, thereby enabling the identification and classification of illegal content. This innovative approach effectively resolves the issue of legal text recognition. Experimental findings underscore the efficacy of the proposed method, achieving an impressive macro-F1 score of 0.8005, precision of 0.8047, and recall of 0.8014. Furthermore, the article delves into an analysis and discussion of the potential application prospects of the legal text extraction model, grounded in fuzzy language processing and metaphor recognition, within the realm of online environment governance.

Summarization of Text is extracting important information from a body of text and present it in the form of a concise summary. The need for summarization has increased in recent times. The importance of having a simple concise summary of information in the news, business and research domains is paramount. Automatic text summarization is a well known task in the NLP (Natural Language Processing) field. Text Summarization techniques can be large divided into two groups: Extractive summarization and Abstractive summarization. Extractive summarization is based on identifying key sentences or phrases from the source text and grouping them to produce a summary without rewriting or paraphrasing the original text. Abstractive summarization is based on utilizing a deeper understanding of the source text and generating new sentences, not present in the original text, which improves the summary by reducing redundancy and focusing on the meaning of the source text. In this study we implement and compare the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are. We aim to learn the pros and cons of the various techniques used by utilizing summary scoring as well as manual inspection of generated summary.

This article surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding The emphasis of this article is on conceptual representation of objects based on the semantic interpretation of natural language input. Six programs serve as case studies for guiding the course of the article. Within the framework of describing each of these programs, several other programs, ideas, and theories that are relevant to the program in focus are presented. RECENT ADVANCES in natural language processing [NLP] have generated considerable interest within the Artificial Intelligence [AI] and Cognitive Science communities. Within NLP, researchers are trying to produce intelligent computer systems that can read, understand, and respond to various human-oriented texts. Terrorism stories, airline flight schedules, and how to fill ice cube trays are all domains that have been used for NLP programs. In order to understand these texts and others, some way of representing information is needed. A complete understanding of human-oriented prose requires the ability to combine the meanings of many readings in an intelligent manner. Learning through the process of generalization is one such mechanism. The integration of representation and generalization in the domain of NLP is the subject of this article. Physical object understanding is an area in which a variety of representation schemes and generalization methods have been used. In past years, researchers have devised various representation systems for objects that range from very simple