Work

Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

Year: 2012

Type: preprint

Abstract: This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm 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. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1207.1420

Authors: Zettlemoyer, Luke S., Collins, Michael

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: MSPA-MCS: collaborative research: Statistical Learning Methods for Complex Decision Problems in Natural Language Processing, CAREER: Statistical Learning Theory for Natural Language Processing: Theory, Algorithms and Representations

OpenAlex ID: W1496189301

Cites: 23

Cited by: 813

Related to: 10