# Incremental Parsing with Minimal Features Using Bi-Directional LSTM

arXiv: https://arxiv.org/abs/1606.06406 · HTML: https://mlsanity.com/paper/1606.06406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

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

- [From Partial to Strictly Incremental Constituent Parsing](https://mlsanity.com/paper/2402.02782/) (2024)

## Abstract (arXiv)

Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese.