Natural language processing-guided meta-analysis and structure factor database extraction from glass literature

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Natural language processing (NLP) is a branch of artificial intelligence, and is a computational technique which analyzes and synthesizes the natural language written and spoken by humans. The human brain can recognize natural language instantly, but for a machine to understand it, this raw data needs to be processed. Computer applications which work for varying structures of languages can be built using NLP. This process will take unstructured data (human language) as input, and after processing, it will be converted to computer-readable languages. This complex process must follow various attributes and the grammatical structure of natural language. NLP performs syntactic and semantic analysis to identify data patterns hidden in natural language. Syntactic analysis, also called parsing, is a phase whose purpose is to draw the exact meaning of an NLP statement. Formal grammar rules of the underlying language are considered while processing the input. The main aim of parsing is to ensure that the string symbols in natural language comply with the grammar rules. The Graph of Words model is an approach that can be used as a better alternative to the Bag of Words model for text processing. A graph representation of the input documents focuses on word independence by considering the order of words and the distance between relevant words.

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

414 LANGUAGE, VOLUME 67, NUMBER 2 (1991) Brixhe & Lejeune's 1984 Corpus des inscriptions paléo-phrygiennes. Since Phrygian is both closely related to Greek and preserved in part through Greek testimony, N has chosen to present his account as a history of PhrygianGreek linguistic contact. This procedure may explain his vagueness in discussing shared features of the two languages: although he calls them 'common innovations' (10), he seems elsewhere to regard them as the result of borrowing during a hypothesized period of close contact among Greek, Phrygian, and several other IE languages in the south Balkans (5-6). Some of these shared features are quite striking—e.g. factitives in *-oyô like Gk. orthöö 'straighten'. Phr. kakoioi (11, 13), and laryngeal vocalization followed by umlaut in Gk. ónoma, Phr. onoman = [b] not [bh], this development is trivial and proves only that Phrygian did not share the Greek change to [ph] etc.). Pp. 6-10: Common retentions of Greek and Phrygian (e.g. the augment and the thirdperson present middle ending -toi). Pp. 10-13: Common innovations of Greek and Phrygian (e.g. agent nouns in -tä, Gk. autos 'self = Phr. autos 'particular'). Pp. 13-16: Inherited lexical material of Phrygian which is lost or only marginally preserved in Greek. Pp. 16-18: Sometime after the hypothesized period of south Balkan close contact with Greek, the Phrygians migrated to Anatolia, where they are attested in historical times. Contact with speakers ofAnatolian languages led to borrowings into Phrygian (mainly names, e.g. Midas). Pp. 18-19: Greek expansion in the first millennium led to contact and hence Greek loans into Phrygian. Pp. 19-22: Direct attestation of Phrygian lost from the third century B.C.E. through the first century CE. Pp. 22-23: Neo-Phrygian attested in grave inscriptions, still in part uninterpretable . Although Phrygian is unlikely ever to play a significant role in IE historical linguistics, it does, as N remarks in conclusion (23), repay time spent applying to it our knowledge of IE. N's essay is a valuable contribution to the project of understanding Phrygian. [Andrew Garrett, University of Texas, Austin.] Natural language processing technologies in Artificial Intelligence: The science and industry perspective. By Klaus K. Obermeier. Chichester : Ellis Horwood, 1989. Pp. 263. Natural language processing (NLP) is the area of research concerned with the many aspects of 'natural' language communication with computers . In this book Obermeier provides an introduction to NLP from both commercial and academic perspectives, primarily aimed at businessmen and data-processing professionals. His presentation is nontechnical, though very comprehensive . Linguists will find the book an accessible introduction to the practical issues in NLP, which may help to clarify the direction and emphasis in this area of language research. The book's organization is excellent, making it very easy to read, although the typographic and editorial quality are not the best. The twelve chapters are divided into three sections: 'NLP in perspective' (17-92). which provides an introduction to the structure of NLP systems and the approaches and formalisms used to implement them; 'Applications' (93-185), in which six major applications of NLP are described: and 'Bottom line' (187-232), where O presents his own evaluation of the state of NLP research and current systems. A good list of references (239-59) is also included. Much of the material in the first section will be familiar to linguists, since the design of NLP systems is largely based on formal linguistic analyses in the areas ofphonology, morphology, syntax, and semantics. In addition, methods for knowledge representation and reasoning drawn from Artificial Intelligence are explained in relation to their relevance to NLP development. This nontechnical overview of the techniques used in NLP is up-to-date. It includes a discussion of parsing methods ranging from contextfree parsing, unification-based parsing, and ATNs to such knowledge-representation schemes from AI as production rules, semantic networks, and frames, to the new connectionist models. BOOK NOTICES 415 The second section comprises the largest part of the book, with one chapter devoted to each of the applications addressed: natural-language interfaces, machine translation, text processing/ understanding, text generation, speech processing , and writing aids. For each application there is a brief...

Applications in the World Wide Web aggregate vast amounts of information from different data sources. The aggregation process is often implemented with Extract, Transform and Load (ETL) processes. Usually ETL processes require information for aggregation available in structured formats, e. g. XML or JSON. In many cases the information is provided in natural language text which makes the application of ETL processes impractical. Due to the fact that information is provided in natural language, Information Extraction (IE) systems have been evolved. They make use of Natural Language Processing (NLP) tools to derive meaning from natural language text. State-of-the-art NLP tools apply Machine Learning methods. These NLP tools perform on newspapers with good quality, but they drop accuracy in other domains. However, to improve the quality for IE systems in specific domains often NLP tools are trained on domain specific text which is a time consuming process. This paper introduces an approach using a Continuous Integration pipeline for organizing and monitoring the annotation process on domain specific corpora.

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.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Universidade Federal de Sao Carlos (UFSCar), Dept Letras, BR-13565905 Sao Carlos, SP, Brazil

Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

With the growing interest in human-computer interfaces that use natural language in some way, researchers and practitioners who work on these interfaces are finding that two general fields of research, CHI and natural language processing (NLP), are complementary and converging. In the CHI research community, there have been investigations on a number of related issues such as usability of text and graphics in on-line documentation, hypertext, spoken-dialogue interfaces, and language/audio resources. In the NLP research community, there is increasing interest in use of natural language, both spoken and written, in intelligent multimodal and multimedia interfaces, e.g., International Symposium on Spoken Dialogue (ISSD-96), COOP 98 Workshop on The Use of Herbert H. Clark's Models of Language Use for the Design of Cooperative Systems, 1998 AAAI Workshop on Representations for Multi-Modal Human-Computer Interaction, and Coling-ACL'98 Workshop on Content Visualizations and Intermedia Representations (CVIR'98).Some technical issues of possible interest to both communities are:1. For what and under what conditions is NL effective in the human-computer interface? For what types of tasks or communication? How does modality influence its effectiveness? How does its effectiveness in computer media differ from that in traditional forms of communication such as face-to-face conversation and print media? How do performance limitations of NLP technologies (e.g., .speech recognition errors) influence effectiveness?2. What are the critical technical requirements for NLP to be effective in the human-computer interface, e.g., coordination of generated text and graphics, incremental and robust interpretation, and modeling turn-taking and initiative in dialogue? What technical requirements arise in transferring technology developed for one language to systems for users of another language (e.g., languages using different writing systems)? How should effectiveness of NLP technologies be evaluated?

In this paper we discuss some aspects of our development of a natural language (NL) processing system to be used in a virtual reality (VR) environment. Our group is pursuing two lines of work: the Natural Language and Virtual Reality (NLVR) project and the natural language research program. The latter addresses basic research problems in NL processing and the former is an implementation project. The two are, in many ways, parallel and overlapping. The research generates hypotheses that we want to test and the NLVR testbed provides new problems for research.