Natural Language Processing: History, Evolution, Application, and Future Work

Johri, Prashant; Khatri, Sunil Kumar; Al-Taani, Ahmad T.; Sabharwal, Munish; Suvanov, Shakhzod; Kumar, Avneesh

doi:10.1007/978-981-15-9712-1_31

Cited by 34 publications

(15 citation statements)

References 12 publications

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“…KeyBERT is based on BERT (Bidirectional Encoder Representations from Transformers) (Johri et al, 2021), published by Google in 2018, a model that uses a transformer (Wolf et al, 2019) architecture to map words, phrases and text into number vectors that capture their meaning. BERT is a bidirectional model, which makes it able to interpret a word based on the context of the sentence, regardless of whether the relevant information is left or right, unlike leftto-right architectures, which look only at the words preceding the one being processed (Devlin et al, 2018).…”

Section: Keybertmentioning

confidence: 99%

Listening to what the system tells us: Innovative auditing for distributed systems

Pilla

Pareschi

Salzano

et al. 2023

Front. Comput. Sci.

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IntroductionIn recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence.MethodsTo develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system.ResultsWe have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains.DiscussionThe proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.

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Section: Keybertmentioning

confidence: 99%

Listening to what the system tells us: Innovative auditing for distributed systems

Pilla

Pareschi

Salzano

et al. 2023

Front. Comput. Sci.

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“…Historically, NLP techniques were limited to heuristic rule-based processes, which greatly limited their performance [7]. The implementation of machine learning methods, which shifted the approach of language processing from rule-based models to statistical inference, has greatly increased the performance of NLP models.…”

Section: Natural Language Processingmentioning

confidence: 99%

“…Our candidate timelines become Gaussians of intelligence each centered on a sensor's collection time, with heights set by the sensor recommendation scores, and widths set by historical values. Additionally, an intelligence threshold, 𝑉 PS , is selected for the current ISR need based on historically similar needs 7 . We make a simplifying assumption that collections from multiple sensors of the same type (i.e.…”

Section: Persistent Intelligence Satisfactionmentioning

confidence: 99%

Cognitive tip and cue: a novel framework for intelligent automated collection planning

Cerezo Falco,

Carroll,

Alekel

et al. 2023

Geospatial Informatics XIII

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We introduce a novel collection planning and orchestration framework called Cognitive Tip and Cue (CTC). CTC consists of three components: a natural language processing module that extracts entities and collection intent from free-text, a sensor recommendation module that scores sensors modalities based on collection parameters and environmental context, and an optimization module that uses a multi-objective genetic algorithm to evolve optimal data provider selections. CTC learns an association between intelligence, surveillance and reconnaissance needs and the historical performance of similar requests. We demonstrate that CTC generates collection plans that appropriately account for collection constraints while selecting data providers that align with tradecraft intuition.

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“…The first task that the first computers solved in the early stages of the formation of NLP was the task of machine translation, i.e., automatic translation of text from one language to another using a computer. This problem was successfully solved and started to be applied in the mid-1950s, in the past century, for the "Russian-English" pair [2].…”

Section: информатика вычислительная техника и управлениеmentioning

confidence: 99%

Analysis of natural language processing technology: modern problems and approaches

Казакова

Sultanova

2022

Vestnik Donskogo gosudarstvennogo tehničeskogo universiteta

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Introduction. The article presents an overview of modern neural network models for natural language processing. Research into natural language processing is of interest as the need to process large amounts of audio and text information accumulated in recent decades has increased. The most discussed in foreign literature are the features of the processing of spoken language. The aim of the work is to present modern models of neural networks in the field of oral speech processing.Materials and Methods. Applied research on understanding spoken language is an important and far-reaching topic in the natural language processing. Listening comprehension is central to practice and presents a challenge. This study meets a method of hearing detection based on deep learning. The article briefly outlines the substantive aspects of various neural networks for speech recognition, using the main terms associated with this theory. A brief description of the main points of the transformation of neural networks into a natural language is given.Results. A retrospective analysis of foreign and domestic literary sources was carried out alongside with a description of new methods for oral speech processing, in which neural networks were used. Information about neural networks, methods of speech recognition and synthesis is provided. The work includes the results of diverse experimental works of recent years. The article elucidates the main approaches to natural language processing and their changes over time, as well as the emergence of new technologies. The major problems currently existing in this area are considered.Discussion and Conclusions. The analysis of the main aspects of speech recognition systems has shown that there is currently no universal system that would be self-learning, noise-resistant, recognizing continuous speech, capable of working with large dictionaries and at the same time having a low error rate.

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Natural Language Processing: History, Evolution, Application, and Future Work

Cited by 34 publications

References 12 publications

Listening to what the system tells us: Innovative auditing for distributed systems

Listening to what the system tells us: Innovative auditing for distributed systems

Cognitive tip and cue: a novel framework for intelligent automated collection planning

Analysis of natural language processing technology: modern problems and approaches

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