Supertagging: Introduction, learning, and application

TL;DR

This paper reviews the development and application of supertagging, highlighting its role in improving parsing efficiency across formalism like TAG and CCG, and discussing learning techniques and integration into parsing systems.

Contribution

It provides a comprehensive overview of supertagging's evolution, learning methods, and its integration into parsing and semantic parsing, summarizing recent advances in the field.

Findings

Supertagging improves parsing efficiency.

Application to TAG and CCG formalism.

Integration into semantic parsing.

Abstract

Supertagging is an approach originally developed by Bangalore and Joshi (1999) to improve the parsing efficiency. In the beginning, the scholars used small training datasets and somewhat na\"ive smoothing techniques to learn the probability distributions of supertags. Since its inception, the applicability of Supertags has been explored for TAG (tree-adjoining grammar) formalism as well as other related yet, different formalisms such as CCG. This article will try to summarize the various chapters, relevant to statistical parsing, from the most recent edited book volume (Bangalore and Joshi, 2010). The chapters were selected so as to blend the learning of supertags, its integration into full-scale parsing, and in semantic parsing.