# Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

arXiv: https://arxiv.org/abs/1603.04351 · HTML: https://mlsanity.com/paper/1603.04351/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2016.

## Built on by (most cited first, 30 of 55)

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- [Data-driven Parsing Evaluation for Child-Parent Interactions](https://mlsanity.com/paper/2209.13778/) (2022)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)
- [MRL Parsing Without Tears: The Case of Hebrew](https://mlsanity.com/paper/2403.06970/) (2024)
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- [DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI](https://mlsanity.com/paper/2108.12536/) (2021)
- [Graph-Based Decoding for Task Oriented Semantic Parsing](https://mlsanity.com/paper/2109.04587/) (2021)
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## Abstract (arXiv)

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.