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

TL;DR

This paper introduces a Bidirectional LSTM-CRF attention-based model for Chinese word segmentation, leveraging sequence modeling and attention mechanisms to improve segmentation accuracy on benchmark datasets.

Contribution

The paper proposes a novel Bidirectional LSTM-CRF attention-based model that enhances Chinese word segmentation performance over existing neural network methods.

Findings

Outperforms baseline neural network models on PKU and MSRA datasets

Demonstrates the effectiveness of combining bidirectional LSTM with CRF and attention mechanisms

Achieves higher segmentation accuracy than previous methods

Abstract

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.