Difference between revisions of "Linking Mathematical Expressions to Wikipedia"
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| + | | title = Linking Mathematical Expressions to Wikipedia | ||
| + | | date = 2017 | ||
| + | | authors = [[Giovanni Yoko Kristianto]]<br />[[Akiko Aizawa]] | ||
| + | | doi = 10.1145/3057148.3057156 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=3057156 | ||
| + | }} | ||
'''Linking Mathematical Expressions to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Giovanni Yoko Kristianto]] and [[Akiko Aizawa]]. | '''Linking Mathematical Expressions to Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Giovanni Yoko Kristianto]] and [[Akiko Aizawa]]. | ||
== Overview == | == Overview == | ||
This paper addresses the challenge of determining the identity of mathematical expressions in documents by linking these expressions to their corresponding [[Wikipedia]] articles. Math expressions are frequently used to describe important concepts in scientific documents; however, particularly in the case of famous or well-established equations, they are often minimally explained within the documents themselves. Linking to Wikipedia allows readers to obtain additional explanation of these math expressions. This paper proposes a learning-based approach to solve this challenge using common [[features]], such as math and text similarities, as well as the importance of the math expression within the document. Further, authors develop a dataset that allowed us to train and test proposed approach. Experimental results show that learning-based approach achieves a precision of 83.40%, compared with 6.22% for the baseline method (a straightforward application of MathIR). | This paper addresses the challenge of determining the identity of mathematical expressions in documents by linking these expressions to their corresponding [[Wikipedia]] articles. Math expressions are frequently used to describe important concepts in scientific documents; however, particularly in the case of famous or well-established equations, they are often minimally explained within the documents themselves. Linking to Wikipedia allows readers to obtain additional explanation of these math expressions. This paper proposes a learning-based approach to solve this challenge using common [[features]], such as math and text similarities, as well as the importance of the math expression within the document. Further, authors develop a dataset that allowed us to train and test proposed approach. Experimental results show that learning-based approach achieves a precision of 83.40%, compared with 6.22% for the baseline method (a straightforward application of MathIR). | ||
Revision as of 10:55, 7 March 2021
Authors | Giovanni Yoko Kristianto Akiko Aizawa |
|---|---|
Publication date | 2017 |
DOI | 10.1145/3057148.3057156 |
Links | Original |
Linking Mathematical Expressions to Wikipedia - scientific work related to Wikipedia quality published in 2017, written by Giovanni Yoko Kristianto and Akiko Aizawa.
Overview
This paper addresses the challenge of determining the identity of mathematical expressions in documents by linking these expressions to their corresponding Wikipedia articles. Math expressions are frequently used to describe important concepts in scientific documents; however, particularly in the case of famous or well-established equations, they are often minimally explained within the documents themselves. Linking to Wikipedia allows readers to obtain additional explanation of these math expressions. This paper proposes a learning-based approach to solve this challenge using common features, such as math and text similarities, as well as the importance of the math expression within the document. Further, authors develop a dataset that allowed us to train and test proposed approach. Experimental results show that learning-based approach achieves a precision of 83.40%, compared with 6.22% for the baseline method (a straightforward application of MathIR).
