  Solving mathematical word problems (MWPs) automatically is challenging,
primarily due to the semantic gap between human-readable words and
machine-understandable logics. Despite the long history dated back to the1960s,
MWPs have regained intensive attention in the past few years with the
advancement of Artificial Intelligence (AI). Solving MWPs successfully is
considered as a milestone towards general AI. Many systems have claimed
promising results in self-crafted and small-scale datasets. However, when
applied on large and diverse datasets, none of the proposed methods in the
literature achieves high precision, revealing that current MWP solvers still
have much room for improvement. This motivated us to present a comprehensive
survey to deliver a clear and complete picture of automatic math problem
solvers. In this survey, we emphasize on algebraic word problems, summarize
their extracted features and proposed techniques to bridge the semantic gap and
compare their performance in the publicly accessible datasets. We also cover
automatic solvers for other types of math problems such as geometric problems
that require the understanding of diagrams. Finally, we identify several
emerging research directions for the readers with interests in MWPs.
