Parsing Algebraic Word Problems into Equations

@article{KoncelKedziorski2015ParsingAW,
  title={Parsing Algebraic Word Problems into Equations},
  author={Rik Koncel-Kedziorski and Hannaneh Hajishirzi and Ashish Sabharwal and Oren Etzioni and S. Ang},
  journal={Transactions of the Association for Computational Linguistics},
  year={2015},
  volume={3},
  pages={585-597}
}
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… Expand
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References

SHOWING 1-10 OF 36 REFERENCES
Learn to Solve Algebra Word Problems Using Quadratic Programming
TLDR
This paper presents a new algorithm to automatically solve algebra word problems 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. Expand
Solving General Arithmetic Word Problems
TLDR
This is the first algorithmic approach that can handle arithmetic problems with multiple steps and operations, without depending on additional annotations or predefined templates, and it outperforms existing systems, achieving state of the art performance on benchmark datasets of arithmetic word problems. Expand
Learning to Automatically Solve Algebra Word Problems
TLDR
An approach for automatically learning to solve algebra word problems by reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers to the problem text. Expand
Learning to Solve Arithmetic Word Problems with Verb Categorization
TLDR
The paper analyzes the arithmetic-word problems “genre”, identifying seven categories of verbs used in such problems, and reports the first learning results on this task without reliance on predefined templates and makes the data publicly available. Expand
Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification
TLDR
This paper uses higher-order unification to define a hypothesis space containing all grammars consistent with the training data, and develops an online learning algorithm that efficiently searches this space while simultaneously estimating the parameters of a log-linear parsing model. Expand
Discriminative Reranking for Natural Language Parsing
TLDR
The boosting approach to ranking problems described in Freund et al. (1998) is applied to parsing the Wall Street Journal treebank, and it is argued that the method is an appealing alternative-in terms of both simplicity and efficiency-to work on feature selection methods within log-linear (maximum-entropy) models. Expand
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
TLDR
A learning algorithm is described that takes as input a training set of sentences labeled with expressions in the lambda calculus and induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. Expand
A Linear Programming Formulation for Global Inference in Natural Language Tasks
TLDR
This work develops a linear programing formulation for this problem and evaluates it in the context of simultaneously learning named entities and relations to efficiently incorporate domain and task specific constraints at decision time, resulting in significant improvements in the accuracy and the "human-like" quality of the inferences. Expand
Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
TLDR
A new meaning representation language is designed to bridge natural language text and math expressions and a CFG parser is implemented based on 9,600 semi-automatically created grammar rules. Expand
Learning to Parse Database Queries Using Inductive Logic Programming
TLDR
Experimental results with a complete database-query application for U.S. geography show that CHILL is able to learn parsers that outperform a preexisting, hand-crafted counterpart, and provide direct evidence of the utility of an empirical approach at the level of a complete natural language application. Expand
...
1
2
3
4
...