Natural Language-Based Self-learning Feedback Analysis System

2015 ◽  
pp. 99-107
Author(s):  
Pratik K. Agrawal ◽  
Abrar S. Alvi ◽  
G. R. Bamnote
Keyword(s):  
Natural Language ◽  
Feedback Analysis ◽  
Analysis System ◽  
Self Learning

DARR: A Free-text Analysis System for the Automatic Documentation of Radiological Reports

1977 ◽  
Vol 16 (03) ◽  
pp. 144-153 ◽  
Author(s):  
E. Vaccari ◽  
W. Delaney ◽  
A. Chiesa
Keyword(s):  
Natural Language ◽  
Text Analysis ◽  
Automatic Documentation ◽  
Free Text ◽  
Software System ◽  
Automatic Translation ◽  
Radiological Report ◽  
Analysis System ◽  
Content Processing

A software system for the automatic free-text analysis and retrieval of radiological reports is presented. Such software involves: (1) automatic translation of the specific natural language in a formalized metalanguage in order to transform the radiological report in a »normalized report« analyzable by computer; (2) content processing of the normalized report to select desired information. The approach used to accomplish point (1) is described in detail referring to a specific application.

Opinion mining technique for developing student feedback analysis system using lexicon-based approach (OMFeedback)

2019 ◽  
Vol 25 (4) ◽  
pp. 2549-2560 ◽  
Author(s):  
Muslihah Wook ◽  
Noor Afiza Mat Razali ◽  
Suzaimah Ramli ◽  
Norshahriah Abdul Wahab ◽  
Nor Asiakin Hasbullah ◽  
...  
Keyword(s):  
Opinion Mining ◽  
Student Feedback ◽  
Feedback Analysis ◽  
Mining Technique ◽  
Analysis System

scholarly journals Aspect Based Sentiments from Tweets using Co-Ranking Multi-Modal Natural Language Processing Methodologies

2020 ◽  
Vol 8 (5) ◽  
pp. 1061-1068
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Detection System ◽  
Word Segmentation ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Sentiment Detection ◽  
Analysis System ◽  
Speech Tagging

Now-a-days people interest to spend their time in social sites especially twitters to post lot of tweets in every day. The posted tweets are used by many users to get the knowledge about the particular applications, products and other search engine queries. With the help of the posted tweets, their emotions and sentiments are derived which are used to get opinion about particular event. Lot of traditional sentiment detection system that has been developed but they failed to analyze huge volume of tweets and online contents with temporal patterns were also difficult to analyze. To overcome the above issues, the co-ranking multi-modal natural language processing based sentiment analysis system was developed to detect the emotions from the posted tweets. Initially, tweets of different events are collected from social sites which are processed by natural language procedures such as Stemming, Lemmatization, Part-of-speech tagging, word segmentation and parsing are applied to get the words related to posted tweets for deriving the sentiments. From the extracted emotions, co-ranking process is applied to get the opinion effectively related to particular event. Then the efficiency of the system is examined using experimental results and discussions. The introduced system recognize the sentiments from tweets with 98.80% of accuracy.

Software architecture of the question-answering subsystem with elements of self-learning

2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 88-95
Author(s):  
Hlybovets A ◽  
◽  
Tsaruk A ◽  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Question Answering ◽  
Building Blocks ◽  
Knowledge Bases ◽  
Learning Technologies ◽  
Software Systems ◽  
Question Answering Systems ◽  
Self Learning

Within the framework of this paper, the analysis of software systems of question-answering type and their basic architectures has been carried out. With the development of machine learning technologies, creation of natural language processing (NLP) engines, as well as the rising popularity of virtual personal assistant programs that use the capabilities of speech synthesis (text-to-speech), there is a growing need in developing question-answering systems which can provide personalized answers to users' questions. All modern cloud providers proposed frameworks for organization of question answering systems but still we have a problem with personalized dialogs. Personalization is very important, it can put forward additional demands to a question-answering system’s capabilities to take this information into account while processing users’ questions. Traditionally, a question-answering system (QAS) is developed in the form of an application that contains a knowledge base and a user interface, which provides a user with answers to questions, and a means of interaction with an expert. In this article we analyze modern approaches to architecture development and try to build system from the building blocks that already exist on the market. Main criteria for the NLP modules were: support of the Ukrainian language, natural language understanding, functions of automatic definition of entities (attributes), ability to construct a dialogue flow, quality and completeness of documentation, API capabilities and integration with external systems, possibilities of external knowledge bases integration After provided analyses article propose the detailed architecture of the question-answering subsystem with elements of self-learning in the Ukrainian language. In the work you can find detailed description of main semantic components of the system (architecture components)

Design of Multi-Parameter Psychophysiological Feedback Analysis System

2012 ◽  
Vol 263-266 ◽  
pp. 147-151
Author(s):  
Zhang Yong Li ◽  
Yu Jiang ◽  
Ya Dong Liu
Keyword(s):  
Real Time ◽  
Monitoring Program ◽  
Data Communication ◽  
Physiological Parameters ◽  
Lms Algorithm ◽  
Parameter System ◽  
Noise Elimination ◽  
Feedback Analysis ◽  
Adaptive Circuit ◽  
Analysis System

In order to supply an effective and reliable tool for the Psychology research in quantity, a multi-parameters psychophysiological feedback analysis system was designed. It can measure several physiological parameters closely related to psychological factors and can realize automatically controlling test gain using a DAC feedback adaptive circuit. Communication protocol is used to increase reliability of data communication and LMS algorithm was used in the system for noise elimination, real-time and data accuracy can be guaranteed. After the debug and verified, multi-parameter system can collect real-time and stable physiological parameters with a higher precision, it provides a feasible monitoring program for psychological assessment.

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