Solving logic puzzles: From robust processing to precise semantics

By Iddo Lev, Bill MacCartney, Christopher D. Manning, Roger Levy. 2004

38 citations recorded by the source corpus.

This paper presents intial work on a system that bridges from robust, broad-coverage natural language processing to precise semantics and automated reasoning, focusing on solving logic puzzles drawn from sources such as the Law School Admission Test (LSAT) and the analytic section of the Graduate Record Exam (GRE). We highlight key challenges, and discuss the representations and performance of the prototype system.

Subfields: Artificial Intelligence, Political Science and International Relations, Natural Language Processing

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Citations in the corpus

Chain of Thought Prompting Elicits Reasoning in Large Language Models (2022, neutral): “Numeric and logical reasoning has been a long-studied task in machine learning and natural language processing (Lev et al., 2004, inter alia ).”
Reasoning about Quantities in Natural Language (2015, methodology): “Approaches range from using rule-based methods (Bobrow, 1964; Lev et al., 2004; Mukherjee and Garain, 2008) to recent template matching techniques (Kushman et al., 2014) .”
Learning to Automatically Solve Algebra Word Problems (2014, background): “The dominant existing approach is to hand engineer rule-based systems to solve math problem in spe-ciﬁc domains (Mukherjee and Garain, 2008; Lev et al., 2004).”
Learn to Solve Algebra Word Problems Using Quadratic Programming (2015, methodology): “The word problem solver is traditionally created by the rule-based approach (Lev et al., 2004; Mukherjee and Garain, 2008; Matsuzaki et al., 2013).”
Computational Approaches to Sentence Completion (2012, methodology): “Logic puzzles drawn from the Law School Administration Test (LSAT) and the GRE were studied in (Lev et al., 2004), which combined an extensive array of techniques to solve the problems.”
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers (2018, background): “Furthermore, logic puzzles are addressed in [101] by transforming robust natural language to precise semantics.”
Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution (2019, background): “Numeric expressions play an important role in various tasks, including textual entailment (Lev et al., 2004; Dagan et al., 2013), solving arithmetic problems (Roy and Roth, 2015), numeric reasoning (Roy et al., 2015; Trask et al., 2018) and language modeling (Spithourakis and Riedel, 2018).”