# Optimizing for Measure of Performance in Max-Margin Parsing

arXiv: https://arxiv.org/abs/1709.01562 · HTML: https://mlsanity.com/paper/1709.01562/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 1 of 1)

- [Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators](https://mlsanity.com/paper/2606.15280/) (2026)

## Abstract (arXiv)

Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approached by means of structured prediction methods. An appealing property of the corresponding discriminative learning algorithms is their ability to integrate the loss function of interest directly into the optimization process, which potentially can increase the resulting performance accuracy. Here, we demonstrate on the example of constituency parsing how to optimize for F1-score in the max-margin framework of structural SVM. In particular, the optimization is with respect to the original (not binarized) trees.