Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Punctuation-aware treebank tree binarization

Eitan Klinger, Vivaan Wadhwa, Jungyeul Park

TL;DR

This work tackles the distortion caused by dropping punctuation in treebank binarization by introducing a punctuation-aware binarization pipeline that keeps punctuation as sibling syntax elements prior to binarization. The method uses a deterministic, invertible transformation with intermediate @X markers to preserve boundary cues, producing binary trees that remain faithful to the original structures and align better with derivational formalisms such as CCG. Empirical results on the Penn Treebank show substantial gains in head–child identification accuracy ($73.66\% \rightarrow 91.85\%$ with the punctuation-aware approach) and competitive alignment with CCGbank, while guaranteeing full reversibility and cross-resource interoperability. The contributions include reproducible code, metadata artifacts, and a versatile framework adaptable to multiple languages, enhancing reliability, transparency, and extensibility of treebank preprocessing.

Abstract

This article presents a curated resource and evaluation suite for punctuation-aware treebank binarization. Standard binarization pipelines drop punctuation before head selection, which alters constituent shape and harms head-child identification. We release (1) a reproducible pipeline that preserves punctuation as sibling nodes prior to binarization, (2) derived artifacts and metadata (intermediate @X markers, reversibility signatures, alignment indices), and (3) an accompanying evaluation suite covering head-child prediction, round-trip reversibility, and structural compatibility with derivational resources (CCGbank). On the Penn Treebank, punctuation-aware preprocessing improves head prediction accuracy from 73.66\% (Collins rules) and 86.66\% (MLP) to 91.85\% with the same classifier, and achieves competitive alignment against CCGbank derivations. All code, configuration files, and documentation are released to enable replication and extension to other corpora.

Punctuation-aware treebank tree binarization

TL;DR

This work tackles the distortion caused by dropping punctuation in treebank binarization by introducing a punctuation-aware binarization pipeline that keeps punctuation as sibling syntax elements prior to binarization. The method uses a deterministic, invertible transformation with intermediate @X markers to preserve boundary cues, producing binary trees that remain faithful to the original structures and align better with derivational formalisms such as CCG. Empirical results on the Penn Treebank show substantial gains in head–child identification accuracy ( with the punctuation-aware approach) and competitive alignment with CCGbank, while guaranteeing full reversibility and cross-resource interoperability. The contributions include reproducible code, metadata artifacts, and a versatile framework adaptable to multiple languages, enhancing reliability, transparency, and extensibility of treebank preprocessing.

Abstract

This article presents a curated resource and evaluation suite for punctuation-aware treebank binarization. Standard binarization pipelines drop punctuation before head selection, which alters constituent shape and harms head-child identification. We release (1) a reproducible pipeline that preserves punctuation as sibling nodes prior to binarization, (2) derived artifacts and metadata (intermediate @X markers, reversibility signatures, alignment indices), and (3) an accompanying evaluation suite covering head-child prediction, round-trip reversibility, and structural compatibility with derivational resources (CCGbank). On the Penn Treebank, punctuation-aware preprocessing improves head prediction accuracy from 73.66\% (Collins rules) and 86.66\% (MLP) to 91.85\% with the same classifier, and achieves competitive alignment against CCGbank derivations. All code, configuration files, and documentation are released to enable replication and extension to other corpora.

Paper Structure

This paper contains 13 sections, 2 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology: punctuation-aware binarization
  3. Transformation procedure
  4. Structural representation
  5. Example
  6. Properties and reversibility
  7. Generality and adaptation
  8. Empirical evaluation of punctuation-aware binarization
  9. Head–child prediction
  10. Reversibility and structural integrity
  11. Alignment with derivation-based schemes
  12. Summary of findings
  13. Conclusion

Figures (3)

  • Figure 1: Excluding punctuation during binarization distorts constituent structure (b), corrected by including punctuation as a syntactic sibling (c).
  • Figure 2: Restructuring rules applied before binarization to attach punctuation as syntactic siblings. $\blacktriangleright$ indicates left-attaching punctuation (e.g., opening quotation marks or parentheses), and $\blacktriangleleft$ indicates right-attaching punctuation (e.g., commas or periods).
  • Figure 3: Example of punctuation attachment and @X node marking.