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System Description: Statistical Parsing of Informalized Mizar Formulas

Cezary Kaliszyk, Josef Urban, Jirí Vyskocil

19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, IEEE Computer Society pp. 169 – 172, 2017.

Abstract

We describe a statistical system that learns parsing of ambiguous Mizar-like formulas from a large training corpus of aligned informal/formal formulas. We describe the methodology and the overall ideas, evaluate the performance of the system, and provide a public web interface for using the system.

 

  PDF |    doi:10.1109/SYNASC.2017.00036  

BibTeX 

@Article{ckjujv-synasc17,
author = {Cezary Kaliszyk and Josef Urban and Ji\v{r}\'{\i} Vysko\v{c}il},
title = {System Description: Statistical Parsing of Informalized {M}izar Formulas},
editor = {Tudor Jebelean, Viorel Negru, Dana Petcu, Daniela Zaharie, Tetsuo Ida, Stephen M. Watt},
booktitle = {19th International Symposium on Symbolic and Numeric Algorithms for
Scientific Computing, SYNASC 2017},
year = {2017},
publisher = {{IEEE} Computer Society},
pages = {169--172}
}
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