English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Dual decomposition, and more generally Lagrangian relaxation, is a classicalmethod for combinatorial optimization; it has recently been applied to severalinference problems in natural language processing (NLP). This tutorial gives anoverview of the technique. We describe example algorithms, describe formalguarantees for the method, and describe practical issues in implementing thealgorithms. While our examples are predominantly drawn from the NLP literature,the material should be of general relevance to inference problems in machinelearning. A central theme of this tutorial is that Lagrangian relaxation isnaturally applied in conjunction with a broad class of combinatorialalgorithms, allowing inference in models that go significantly beyond previouswork on Lagrangian relaxation for inference in graphical models.

A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing