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

Avani Chandurkar, Ajay Bansal

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)345-371
Number of pages27
JournalInternational Journal of Semantic Computing
Volume11
Issue number3
DOIs
StatePublished - Sep 1 2017

Keywords

  • information retrieval
  • natural language processing
  • question answering system
  • Structured semantic data

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Linguistics and Language
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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