FindThatQuote: A Question-Answering Web-based System to Locate Quotes using Deep Learning and Natural-Language Processing

2021 ◽  
Author(s):  
Nathan Ji ◽  
Yu Sun
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Success Rate ◽  
Language Processing ◽  
Question Answering ◽  
Specific Information ◽  
Web Based ◽  
User Friendly ◽  
Web Based System

The digital age gives us access to a multitude of both information and mediums in which we can interpret information. A majority of the time, many people find interpreting such information difficult as the medium may not be as user friendly as possible. This project has examined the inquiry of how one can identify specific information in a given text based on a question. This inquiry is intended to streamline one's ability to determine the relevance of a given text relative to his objective. The project has an overall 80% success rate given 10 articles with three questions asked per article. This success rate indicates that this project is likely applicable to those who are asking for content level questions within an article.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Completely Automated Captcha Solver

2021 ◽  
Vol 9 (VII) ◽  
pp. 1728-1732
Author(s):  
Prof. P. Y. Pawar
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Success Rate ◽  
Language Processing ◽  
Turing Test ◽  
Automated System ◽  
High Success Rate ◽  
First Line

This project was primarily aimed to create an automated system for solving captcha’s automatically. CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Human Apart) are the Internet’s first line of defence against automated account creation and service abuse. This paper presents unCaptcha, an automates system that can solve Captcha’s most difficult auditory challenges with high success rate using Deep Learning and Natural Language processing. There are four types of Captcha’s Audio Captcha,Text based captcha, Image captcha,Maths-solver captcha.

scholarly journals Deep learning approach to text analysis for human emotion detection from big data

2022 ◽  
Vol 31 (1) ◽  
pp. 113-126
Author(s):  
Jia Guo
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Question Answering ◽  
Word Embeddings ◽  
Emotion Detection ◽  
Human Emotion

Abstract Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings.

scholarly journals Analysis on the Language Independent and Dependent Aspects of Deep Learning based Question Answering Systems

2020 ◽  
Vol 9 (4) ◽  
pp. 2057-2062
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
Question Answering ◽  
Natural Languages ◽  
Learning Network ◽  
The Difference ◽  
Deep Learning Network

Natural languages are ambiguous and computers are not capable of understanding the natural languages in the way people really understand them. Natural Language Processing (NLP) is concerned with the development of computational models based on the aspects of human language processing. Question Answering (QA) system is a field of Natural Language Processing that provides precise answer for the user question which is given in natural language. In this work, a MemN2N model based question answering system is implemented and its performance is evaluated with a complex question answering tasks using bAbI dataset of three different language text corpuses. The scope of this work is to understand the language independent and dependant aspects of a deep learning network. For this, we will study the performance of the deep learning network by training and testing it with different kinds of question answering tasks with different languages and also try to understand the difference in performance with respect to the languages

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

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