@article{TACL692,
        author = {Koncel-Kedziorski, Rik  and Hajishirzi, Hannaneh  and Sabharwal,
Ashish  and Etzioni, Oren  and Ang, Siena },
        title = {Parsing Algebraic Word Problems into Equations},
        journal = {Transactions of the Association for Computational Linguistics},
        volume = {3},
        year = {2015},
        keywords = {},
        abstract = {This paper formalizes the problem of solving multi-sentence
algebraic word problems as that of generating and scoring equation trees. We
use integer linear programming to generate equation trees and score their
likelihood by learning local and global discriminative models. These models
are trained on a small set of word problems and their answers, without any
manual annotation, in order to choose the equation that best matches the
problem text. We refer to the overall system as ALGES.  We compare ALGES
with previous work and show that it covers the full gamut of arithmetic
operations whereas Hosseini et al. (2014) only handle addition and
subtraction. In addition, ALGES overcomes the brittleness of the Kushman et
al. (2014) approach on single-equation problems, yielding a 15% to 50%
reduction in error.},
        issn = {2307-387X},
        url =
{https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/692},
        pages = {585--597}
}