Semantification of Identifiers in Mathematics for Better Math Information Retrieval

Published

July 17, 2016

Author(s)

Moritz Schubotz, Alexey Grigoriev, Howard Cohl, Norman Meushke, Bela Gipp, Volker Markl

Abstract

Mathematical formulae are essential in science, but face challenges of ambiguity, due to the use of a small number of identifiers to represent an immense number of concepts. Corresponding to word sense disambiguation in Natural Language Processing, we disambiguate mathematical identifiers. By regarding formulae and natural text as one monolithic information source, we are able to extract the semantics of identifiers in a process we term \emphMathematical Language Processing} (MLP). As scientific communities tend to establish standard (identifier) notations, we use the document domain to infer the actual meaning of an identifier. Therefore, we adapt the software development concept of namespaces to mathematical notation. Thus, we learn namespace definitions by clustering the MLP results and mapping those clusters to subject classification schemata. In addition, this gives fundamental insights into the usage of mathematical notations in science, technology, engineering and mathematics. Our gold standard based evaluation shows that MLP extracts relevant identifier-definitions. Moreover, we discover that identifier namespaces improve the performance of automated identifier-definition extraction, and elevate it to a level that cannot be achieved within the document context alone.

Proceedings Title

ACM SIGIR Conference on Research and Development in Information Retrieval

Conference Dates

July 17-21, 2016

Conference Location

Pisa, IT

Pub Type

Conferences

Download Paper

https://doi.org/10.1145/2911451.2911503

Local Download

Keywords

Information Retrieval

Citation

Schubotz, M. , Grigoriev, A. , Cohl, H. , Meushke, N. , Gipp, B. and Markl, V. (2016), Semantification of Identifiers in Mathematics for Better Math Information Retrieval, ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, IT, [online], https://doi.org/10.1145/2911451.2911503, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920220 (Accessed December 26, 2024)