# Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation

arXiv: https://arxiv.org/abs/2105.09681 · HTML: https://mlsanity.com/paper/2105.09681/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Memory Networks](https://mlsanity.com/paper/1410.3916/) (2014)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017)
- [Incorporating Transformer and LSTM to Kalman Filter with EM algorithm for state estimation](https://mlsanity.com/paper/2105.00250/) (2021)

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

- [Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction](https://mlsanity.com/paper/2204.02623/) (2022)
- [Differential equation and probability inspired graph neural networks for latent variable learning](https://mlsanity.com/paper/2202.13800/) (2022)

## Abstract (arXiv)

Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP). The quality of word segmentation will directly affect the rest of NLP tasks. Recently, with the artificial intelligence tide rising again, Long Short-Term Memory (LSTM) neural network, as one of easily modeling in sequence, has been widely utilized in various kinds of NLP tasks, and functions well. Attention mechanism is an ingenious method to solve the memory compression problem on LSTM. Furthermore, inspired by the powerful abilities of bidirectional LSTM models for modeling sequence and CRF model for decoding, we propose a Bidirectional LSTM-CRF Attention-based Model in this paper. Experiments on PKU and MSRA benchmark datasets show that our model performs better than the baseline methods modeling by other neural networks.