About: Relation/Entity-Centric Reading Comprehension

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Relation/Entity-Centric Reading Comprehension
Creator	Sasaki, Yutaka Gimpel, Kevin Mcallester, David Miwa, Makoto Onishi, Takeshi
source	ArXiv
abstract	Constructing a machine that understands human language is one of the most elusive and long-standing challenges in artificial intelligence. This thesis addresses this challenge through studies of reading comprehension with a focus on understanding entities and their relationships. More specifically, we focus on question answering tasks designed to measure reading comprehension. We focus on entities and relations because they are typically used to represent the semantics of natural language.
has issue date	2020-08-27(xsd:dateTime)
has license	arxiv
sha1sum (hex)	00b4046cfc7f94d35e4fcf5af4cac34323812034
resource representing a document's title	Relation/Entity-Centric Reading Comprehension
resource representing a document's body	covid:00b4046cfc7f94d35e4fcf5af4cac34323812034#body_text
is schema:about of	named entity 'READING COMPREHENSION' named entity 'knowledge base' named entity 'probability distribution' named entity 'emergent structure' named entity 'Japan' »more»

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