The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> GEOMETRIC WORD PROBLEM (GWP) SOLVER <s> Abstract A connection diagram understanding system using a facsimile as its input device has been designed and implemented. The principle of this system is described and examples of its application to some hand-sketched diagrams are shown. In order to reduce processing time, the procedure is started by only accessing the pixels located on the borders of certain meshes to detect characteristic patterns and make a control map. Background judgment and long straight line segment extraction are executed on the control map. Other complicated areas, which are usually a small portion of the whole diagram area, are also indicated by special labels on the map and then processed by a detailed procedure which scans every pixel at these areas. Graph descriptions of diagrams are employed at different steps of the hierarchical understanding. Problems of data compression, diagram retrieval, and diagram editing are discussed. <s> BIB001 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> GEOMETRIC WORD PROBLEM (GWP) SOLVER <s> We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including either full equations or just the final answers. We evaluate performance on a newly gathered corpus of algebra word problems, demonstrating that the system can correctly answer almost 70% of the questions in the dataset. This is, to our knowledge, the first learning result for this task. <s> BIB002 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> GEOMETRIC WORD PROBLEM (GWP) SOLVER <s> This paper presents a new algorithm to automatically solve algebra word problems. Our algorithm solves a word problem via analyzing a hypothesis space containing all possible equation systems generated by assigning the numbers in the word problem into a set of equation system templates extracted from the training data. To obtain a robust decision surface, we train a log-linear model to make the margin between the correct assignments and the false ones as large as possible. This results in a quadratic programming (QP) problem which can be efficiently solved. Experimental results show that our algorithm achieves 79.7% accuracy, about 10% higher than the state-of-the-art baseline (Kushman et al., 2014). <s> BIB003
Geometry solvers have been studied for a long history. Visual diagram understanding is a sub-domain that has attracted significant attention. As an early work for understanding line drawings, BIB001  For "each ride cost 5 tickets", the quantity "5" is a rate Is between 0 and 1 BIB003 [45] Is equal to one or two BIB002 [40]  [13]