Comparative Question Answering System based on Natural Language Processing and Machine Learning

2021 ◽  
Author(s):  
Rohit Arora ◽  
Parth Singh ◽  
Hemlata Goyal ◽  
Sunita Singhal ◽  
Smita Vijayvargiya
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Question Answering System

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

scholarly journals Ontology-Based Question Answering System for an Academic Domain

2021 ◽  
Author(s):  
García-Robledo Gabriela A ◽  
Reyes-Ortiz José A ◽  
González-Beltrán Beatriz A ◽  
Bravo Maricela
Keyword(s):  
Natural Language Processing ◽  
User Interface ◽  
Speech Recognition ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Question Answering System ◽  
Methods And Techniques

The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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