scholarly journals Desktop Voice Assistant Using Natural Language Processing (NLP)

2020 ◽  
Vol 6 (12) ◽  
pp. 332-335
Author(s):  
Lalit Kumar
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cloud Services ◽  
Home Automation ◽  
The People ◽  
The Future ◽  
The Voice ◽  
The Way

Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. the voice assistant was first introduced on smartphones and after the popularity it got. It was widely accepted by all. Initially, the voice assistant was mostly being used in smartphones and laptops but now it is also coming as home automation and smart speakers. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.

PRINCIPAL PROBLEMS OF NATURAL LANGUAGE PROCESSING SYSTEMS

2018 ◽  
pp. 35-38
Author(s):  
O. Hyryn
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Syntactic Analysis ◽  
Syntactic Ambiguity ◽  
Grammatical Structure ◽  
English Sentence ◽  
Analysis Methods ◽  
The Way

The article deals with natural language processing, namely that of an English sentence. The article describes the problems, which might arise during the process and which are connected with graphic, semantic, and syntactic ambiguity. The article provides the description of how the problems had been solved before the automatic syntactic analysis was applied and the way, such analysis methods could be helpful in developing new analysis algorithms. The analysis focuses on the issues, blocking the basis for the natural language processing — parsing — the process of sentence analysis according to their structure, content and meaning, which aims to analyze the grammatical structure of the sentence, the division of sentences into constituent components and defining links between them.

Automated Writing Assistance

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Writing Process ◽  
Modern Society ◽  
The Future ◽  
The 1960S ◽  
Processing Techniques

The ability to communicate in writing is an essential skill in modern society. But ability in writing varies considerably; and no matter what their existing level of competence, most writers would acknowledge that what they write could often be improved. Given that the output of the writing process is natural language, it seems plausible that natural language processing techniques might be used to analyse this output and to suggest ways to improve it. In various guises, this has indeed been an application of NLP at least since the 1960s. In this chapter, we survey the different kinds of assistance to authors that NLP makes possible; we describe what can be done today, and explore what might be possible in the future.

scholarly journals Natural Language Processing for Lawyers and Judges

2021 ◽  
pp. 1399
Author(s):  
Frank Fagan
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Legal Analysis ◽  
The Future

A Review of Law as Data: Computation, Text, & the Future of Legal Analysis. Edited by Michael A. Livermore and Daniel N. Rockmore.

scholarly journals Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

2020 ◽  
Vol 12 (19) ◽  
pp. 7848 ◽  
Author(s):  
Israel Griol-Barres ◽  
Sergio Milla ◽  
Antonio Cebrián ◽  
Huaan Fan ◽  
Jose Millet
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Weak Signals ◽  
Remote Sensors ◽  
The Future ◽  
Increasing Demand ◽  
New Applications ◽  
Start Ups

Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

Automated Writing Assistance

2014 ◽  
Author(s):  
Robert Dale
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Writing Process ◽  
Modern Society ◽  
The Future ◽  
The 1960S ◽  
Processing Techniques

The ability to communicate in writing is an essential skill in modern society. But ability in writing varies considerably; and no matter what their existing level of competence, most writers would acknowledge that what they write could often be improved. Given that the output of the writing process is natural language, it seems plausible that natural language processing techniques might be used to analyse this output and to suggest ways to improve it. In various guises, this has indeed been an application of NLP at least since the 1960s. In this chapter, we survey the different kinds of assistance to authors that NLP makes possible; we describe what can be done today, and explore what might be possible in the future.

Development of Natural Language Processing based Communication and Educational Assisted Systems for the People with Hearing Disability in Myanmar

2019 ◽  
Vol 20 (48) ◽  
Author(s):  
Swe Zin Moe ◽  
Ye Kyaw Thu ◽  
Hlaing Myat Nwe ◽  
Hnin Wai Wai Hlaing ◽  
Ni Htwe Aung ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Hearing Disability ◽  
The People

scholarly journals Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Tiberiu-Marian Georgescu
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Named Entity Recognition ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Model Based ◽  
The Way

This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.

scholarly journals Foundational ontologies meet ontology matching: A survey

Semantic Web ◽  
2021 ◽  
pp. 1-20
Author(s):  
Cassia Trojahn ◽  
Renata Vieira ◽  
Daniela Schmidt ◽  
Adam Pease ◽  
Giancarlo Guizzardi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Reference Model ◽  
Research Area ◽  
Ontology Matching ◽  
The Future

Ontology matching is a research area aimed at finding ways to make different ontologies interoperable. Solutions to the problem have been proposed from different disciplines, including databases, natural language processing, and machine learning. The role of foundational ontologies for ontology matching is an important one, as they provide a well-founded reference model that can be shared across domains. It is multifaceted and with room for development. This paper presents an overview of the different tasks involved in ontology matching that consider foundational ontologies. We discuss the strengths and weaknesses of existing proposals and highlight the challenges to be addressed in the future.

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