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.

scholarly journals Building Graph for Events and Time in Natural Language Text

2020 ◽  
Vol 9 (3) ◽  
pp. 581-586
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Relation Extraction ◽  
Event Extraction ◽  
Event Time ◽  
Time Graph ◽  
Question Answering Systems

Events and time are two major key terms in natural language processing due to the various event-oriented tasks these are become an essential terms in information extraction. In natural language processing and information extraction or retrieval event and time leads to several applications like text summaries, documents summaries, and question answering systems. In this paper, we present events-time graph as a new way of construction for event-time based information from text. In this event-time graph nodes are events, whereas edges represent the temporal and co-reference relations between events. In many of the previous researches of natural language processing mainly individually focused on extraction tasks and in domain-specific way but in this work we present extraction and representation of the relationship between events- time by representing with event time graph construction. Our overall system construction is in three-step process that performs event extraction, time extraction, and representing relation extraction. Each step is at a performance level comparable with the state of the art. We present Event extraction on MUC data corpus annotated with events mentions on which we train and evaluate our model. Next, we present time extraction the model of times tested for several news articles from Wikipedia corpus. Next is to represent event time relation by representation by next constructing event time graphs. Finally, we evaluate the overall quality of event graphs with the evaluation metrics and conclude the observations of the entire work

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 Natural Language Processing: Components, Advances, Tools and Industrial Applications

2021 ◽  
Vol 9 (9) ◽  
pp. 964-971
Author(s):  
Hima Yeldo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Industrial Applications ◽  
Spam Detection ◽  
Language Understanding ◽  
Language Generation ◽  
Email Spam

Abstract: Natural Language Processing is the study that focuses the interplay between computer and the human languages NLP has spread its applications in various fields such as an email Spam detection, machine translation, summation, information extraction, and question answering etc. Natural Language Processing classifies two parts i.e. Natural Language Generation and Natural Language understanding which evolves the task to generate and understand the text.

Mining Profiles and Definitions with Natural Language Processing

2008 ◽  
pp. 77-98
Author(s):  
Horacio Saggion
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Text Summarization ◽  
Processing Technology ◽  
Free Text ◽  
Human Knowledge ◽  
Methods And Techniques

Free text is a main repository of human knowledge, therefore methods and techniques to access this unstructured source of knowledge are of paramount importance. In this chapter we describe natural language processing technology for the development of question answering and text summarization systems. We focus on applications aiming at mining textual resources to extract knowledge for the automatic creation of definitions and person profiles.

scholarly journals A question answering system developed as a project in a natural language processing course

2000 ◽  
Author(s):  
W. Wang ◽  
J. Auer ◽  
R. Parasuraman ◽  
I. Zubarev ◽  
D. Brandyberry ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Question Answering System
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