Template-based Question Answering analysis on the LC-QuAD2.0 Dataset

Akshay Kumar Dileep, Anurag Mishra, Ria Mehta, Siddharth Uppal, Jaydeep Chakraborty, Srividya K. Bansal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, template-based question answer has picked up steam as a solution for evaluating RDF triples. Once we delve into the domain of template-based question answering, two important questions arise which are, the size of the dataset used as the knowledge base and the process of training used on that knowledge base. Previous studies attempted this problem with the LC-QuAD dataset and recursive neural network for training. This paper studies the same problem with a larger and newer benchmark dataset called LC-QuAD 2.0 and training using different machine learning models. The objective of this paper is to provide a comparative study using the newer LC-QuAD 2.0 dataset that has an updated schema and 30,000 question-answer pairs. Our study will focus on using and comparing two Machine Learning models and 3 different pre-processing techniques to generate results and identify the best model for this problem.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages443-448
Number of pages6
ISBN (Electronic)9781728188997
DOIs
StatePublished - Jan 2021
Externally publishedYes
Event15th IEEE International Conference on Semantic Computing, ICSC 2021 - Virtual, Laguna Hills, United States
Duration: Jan 27 2021Jan 29 2021

Publication series

NameProceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

Conference

Conference15th IEEE International Conference on Semantic Computing, ICSC 2021
CountryUnited States
CityVirtual, Laguna Hills
Period1/27/211/29/21

Keywords

  • Linked Data
  • Machine Learning
  • Question Answering
  • Template-based question answering

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)

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