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grammar learning

Grammar learning is a computational process within machine learning and natural language processing that automatically acquires the formal rules, structural dependencies, and syntactic patterns governing a language from sample data. In structured prediction and computational linguistics, it involves analyzing sequences of symbols, such as words or tokens, to infer hierarchical parse trees, dependency relations, or generative production rules. The process can be conducted through supervised methods that learn mappings between input strings and annotated syntactic representations, or through unsupervised induction that discovers underlying structural regularities directly from unannotated text. By capturing the organizational constraints of sequential data, grammar learning enables automated parsing, sequence alignment, language modeling, and the interpretation of both natural and artificial languages.

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Support vector machine learning for interdependent and structured output spaces

Support vector machine learning for interdependent and structured output spaces

Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun

OrganizationsBrown UniversityCornell University

Why you should read this

Presents a maximum-margin framework that extends Support Vector Machines to complex, structured prediction tasks and solves the resulting exponential-sized optimization problem via an efficient cutting-plane algorithm.

Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

Added

2026-09-25