A short survey on end-to-end simple question answering systems

2020 ◽  
Vol 53 (7) ◽  
pp. 5429-5453
Author(s):  
José Wellington Franco da Silva ◽  
Amanda Drielly Pires Venceslau ◽  
Juliano Efson Sales ◽  
José Gilvan Rodrigues Maia ◽  
Vládia Célia Monteiro Pinheiro ◽  
...  
Keyword(s):  
Question Answering ◽  
Short Survey ◽  
Question Answering Systems ◽  
Simple Question ◽  
End To End

scholarly journals End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems

2020 ◽  
Author(s):  
Siamak Shakeri ◽  
Cicero Nogueira dos Santos ◽  
Henghui Zhu ◽  
Patrick Ng ◽  
Feng Nan ◽  
...  
Keyword(s):  
Question Answering ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
Data Generation ◽  
Synthetic Data Generation ◽  
Question Answering Systems ◽  
End To End

Improved Entity Linking for Simple Question Answering Over Knowledge Graph

2021 ◽  
Vol 31 (01) ◽  
pp. 55-80
Author(s):  
Kai Chen ◽  
Guohua Shen ◽  
Zhiqiu Huang ◽  
Haijuan Wang
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Ranking Algorithm ◽  
Entity Linking ◽  
Question Answering Systems ◽  
Simple Question ◽  
Knowledge Graphs ◽  
Relation Prediction ◽  
The Impact

Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.

scholarly journals A Bayesian end-to-end model with estimated uncertainties for simple question answering over knowledge bases

2021 ◽  
Vol 66 ◽  
pp. 101167
Author(s):  
Linhai Zhang ◽  
Chao Lin ◽  
Deyu Zhou ◽  
Yulan He ◽  
Meng Zhang
Keyword(s):  
Question Answering ◽  
Knowledge Bases ◽  
Simple Question ◽  
End To End

An Efficient Semantic Analysis Technique for the Question Answering Systems

2019 ◽  
Vol 14 (22) ◽  
pp. 8289-8292
Author(s):  
Ibrahim Mahmoud Ibrahim Alturani ◽  
Mohd Pouzi Bin Hamzah
Keyword(s):  
Question Answering ◽  
Semantic Analysis ◽  
Analysis Technique ◽  
Question Answering Systems

scholarly journals Causal Perception in Question-Answering Systems

2021 ◽  
Author(s):  
Po-Ming Law ◽  
Leo Yu-Ho Lo ◽  
Alex Endert ◽  
John Stasko ◽  
Huamin Qu
Keyword(s):  
Question Answering ◽  
Causal Perception ◽  
Question Answering Systems

scholarly journals A framework for enriching Data Warehouse analysis with Question Answering systems

2014 ◽  
Vol 46 (1) ◽  
pp. 61-82 ◽  
Author(s):  
Antonio Ferrández ◽  
Alejandro Maté ◽  
Jesús Peral ◽  
Juan Trujillo ◽  
Elisa De Gregorio ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Question Answering ◽  
Question Answering Systems

A model for quantitative evaluation of an end-to-end question-answering system

2007 ◽  
Vol 58 (8) ◽  
pp. 1082-1099 ◽  
Author(s):  
Nina Wacholder ◽  
Diane Kelly ◽  
Paul Kantor ◽  
Robert Rittman ◽  
Ying Sun ◽  
...  
Keyword(s):  
Quantitative Evaluation ◽  
Question Answering ◽  
Question Answering System ◽  
End To End

scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

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