# Improved Parsing for Argument-Clusters Coordination

arXiv: https://arxiv.org/abs/1606.00294 · HTML: https://mlsanity.com/paper/1606.00294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction](https://mlsanity.com/paper/2305.14660/) (2023)
- [Conjunct Resolution in the Face of Verbal Omissions](https://mlsanity.com/paper/2305.16740/) (2023)

## Abstract (arXiv)

Syntactic parsers perform poorly in prediction of Argument-Cluster Coordination (ACC). We change the PTB representation of ACC to be more suitable for learning by a statistical PCFG parser, affecting 125 trees in the training set. Training on the modified trees yields a slight improvement in EVALB scores on sections 22 and 23. The main evaluation is on a corpus of 4th grade science exams, in which ACC structures are prevalent. On this corpus, we obtain an impressive x2.7 improvement in recovering ACC structures compared to a parser trained on the original PTB trees.