Difference between revisions of "Improving Accessibility to Mathematical Formulas: the Wikipedia Math Accessor"
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| − | '''Improving Accessibility to Mathematical Formulas: the Wikipedia Math Accessor''' - scientific work related to [[Wikipedia quality]] published in | + | '''Improving Accessibility to Mathematical Formulas: the Wikipedia Math Accessor''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[J. Fuentes Sepúlveda]] and [[Leo Ferres]]. |
== Overview == | == Overview == | ||
| − | Mathematics accessibility is an important topic for inclusive education. | + | Mathematics accessibility is an important topic for inclusive education. In this paper, authors make [[Wikipedia]]'s repository of mathematical formulas accessible by providing a natural language description of its more than 420,000 formulas using a well-researched sub-language. Authors also contribute by targeting Spanish speakers, for whom assistive technologies, particularly domain-specific technologies like the one described here, are scarce. Authors focus on the semantics of formulas rather than their visual appearance allowed us to generate verbalizations with a precision of approximately 80% of understandable descriptions, as shown in an evaluation with sighted users. |
Revision as of 09:37, 21 January 2020
Improving Accessibility to Mathematical Formulas: the Wikipedia Math Accessor - scientific work related to Wikipedia quality published in 2012, written by J. Fuentes Sepúlveda and Leo Ferres.
Overview
Mathematics accessibility is an important topic for inclusive education. In this paper, authors make Wikipedia's repository of mathematical formulas accessible by providing a natural language description of its more than 420,000 formulas using a well-researched sub-language. Authors also contribute by targeting Spanish speakers, for whom assistive technologies, particularly domain-specific technologies like the one described here, are scarce. Authors focus on the semantics of formulas rather than their visual appearance allowed us to generate verbalizations with a precision of approximately 80% of understandable descriptions, as shown in an evaluation with sighted users.
