Generative Model for NLP Applications based on Component Extraction

Bhardwaj, Anupam; Khanna, Pooja; Kumar, Sachin; Pragya,

doi:10.1016/j.procs.2020.03.391

Cited by 10 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A study similar to the aforementioned one is conducted by [8]. Built on significant elements gathered from several case studies, a Generative Model for NLP Applications is pro-vided in this paper.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…Systems like these, although beneficial, are unable to address the problem of physical safety of an individual. • A very similar system is proposed by [8], where the speech detection is done for multiple languages to detect verbal threats, but not physical ones. • Some other systems, like those proposed in [9], [10], [11], and [12], detect emotions and events from texts/speech, social media posts and tweets respectively.…”

Section: Technical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Live Event Detection for People’s Safety Using NLP and Deep Learning

Sen,

Rajakumaran,

Mahdal

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Today, humans pose the greatest threat to society by getting involved in robbery, assault, or homicide activities. Such circumstances threaten the people working alone at night in remote areas especially women. Any such kind of threat in real time is always associated with a sound/noise which may be used for an early detection. Numerous existing measures are available but none of them sounds efficient due to lack of accuracy, delays in exact prediction of threat. Hence a novel software-based prototype is developed to detect threats from a person's surrounding sound/noise and automatically alert the registered contacts of victims by sending email, SMS, WhatsApp messages through their smartphones without any other hardware components. Audio signals from Kaggle dataset are visualized, analyzed using Exploratory Data Analytics (EDA) techniques. By feeding EDA outcomes into various Deep Learning models: Long short-term memory (LSTM), Convolutional Neural Networks (CNN) yields accuracy of 96.6% in classifying the audio-events.

show abstract

Section: Technical Backgroundmentioning

confidence: 99%

Section: Technical Backgroundmentioning

confidence: 99%

Live Event Detection for People’s Safety Using NLP and Deep Learning

Sen,

Rajakumaran,

Mahdal

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…When we consider embedding, we focus on the basic parts of the text, i.e. words [1,6,8,28] and sentences [7,51,57]. In general, word embeddings maps words from the input texts into continuous space vectors [67].…”

Section: Hybrid Solutionsmentioning

confidence: 99%

Trends in Natural Language Understanding with sentence representation and sentiment analysis

Guminska,

Poniszewska-Maranda,

Ochelska-Mierzejewska

2023

Preprint

View full text Add to dashboard Cite

Natural Language Processing (NLP) is a crucial branch of artificial intelligence that examines how people and computers communicate through natural language. The majority of NLP research focuses on the fundamental methods that enable humans to understand and generate words, phrases, sentences, and the whole documents. The primary focus of the studies in this area concentrate on English as the common language used by scientists. Unfortunately, this language has a very straightforward structure. It indicates that most of the techniques that are very successful for documents in English do not work equally well for texts in other niche languages. To investigate the existing studies on Natural Language Understanding (NLU), a Systematic Literature Review (SLR) was performed. Through our deep research on NLU and its extensions, 46 studies were selected mentioning 38 machine learning, 3 hybrid and 4 knowledge-based approaches to NLU. The paper presents the systematized knowledge along with a critical analysis of existing research, both for text processing in English and for other native languages. It shows the solutions and reasoning of researchers, and compiles the advantages and disadvantages of the presented solutions.

show abstract

“…Also, this model helps handle priorities in a structured manner and aids in offering flexibility for specific needs of the project. NLP is one of the text mining methods used for text analysis that implements a unique type of linguistic analysis that essentially makes the machine read and analyze [23]. ML and NLP together make a machine behave intelligently.…”

Section: Internationalmentioning

confidence: 99%

Use of Natural Language Processing in Software Requirements Prioritization – A Systematic Literature Review

Vijayakumar

Nethravathi

2021

IJAEML

View full text Add to dashboard Cite

Purpose: Research involves the creation and implementation of new ideas by keeping the existing work as a foundation. The literature review done in this paper is to familiarise and to know about the domain of research to integrate the existing ideas with the new ones. Methodology: The literature that is required for this study is chosen from multiple secondary sources such as journals, conference proceedings, and web resources. All the pieces of literature are carefully studied and summarised. This is further used to arrive at Research agendas and Research gaps. Findings/Result: It has been observed and understood that Natural Language Processing (NLP) is a field involving analysis and processing of textual contents. It also requires Machine Learning Algorithms to support the processing. This combination has already been used in various domains, the important one being the health sector. EMR data is huge and NLP can successfully process and prioritize them in different dimensions. In that direction, the same concept and technology can be applied to Software Engineering also and Requirements can be prioritized. Originality: This literature review study is carried out using secondary data which is collected through various online sources. The information thus gathered will be used in the future to build upon existing theory and framework or build a new methodology. It is also seen that any conclusion or decision is not biased or unidirectional. A sincere effort is made to identify a research topic to carry out the research. Paper Type: Literature Review.

show abstract

Generative Model for NLP Applications based on Component Extraction

Cited by 10 publications

References 42 publications

Live Event Detection for People’s Safety Using NLP and Deep Learning

Live Event Detection for People’s Safety Using NLP and Deep Learning

Trends in Natural Language Understanding with sentence representation and sentiment analysis

Use of Natural Language Processing in Software Requirements Prioritization – A Systematic Literature Review

Contact Info

Product

Resources

About