# Speeding Up Natural Language Parsing by Reusing Partial Results

## TL;DR

This paper introduces a case-based reasoning technique that reuses partial parse tree results to speed up dependency parsing, achieving over 20% faster processing with minimal accuracy loss.

## Contribution

It presents a novel template-based method for reusing syntactic analysis results to enhance parsing speed while maintaining acceptable accuracy levels.

## Key findings

- Parsing speed increased by over 20%
- Accuracy decreased by less than 3 points in UAS
- Method effective on English language data

## Abstract

This paper proposes a novel technique that applies case-based reasoning in order to generate templates for reusable parse tree fragments, based on PoS tags of bigrams and trigrams that demonstrate low variability in their syntactic analyses from prior data. The aim of this approach is to improve the speed of dependency parsers by avoiding redundant calculations. This can be resolved by applying the predefined templates that capture results of previous syntactic analyses and directly assigning the stored structure to a new n-gram that matches one of the templates, instead of parsing a similar text fragment again. The study shows that using a heuristic approach to select and reuse the partial results increases parsing speed by reducing the input length to be processed by a parser. The increase in parsing speed comes at some expense of accuracy. Experiments on English show promising results: the input dimension can be reduced by more than 20% at the cost of less than 3 points of Unlabeled Attachment Score.