# Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

## TL;DR

This paper introduces a neural Chinese word segmentation method that leverages lexicons and unlabeled data through posterior regularization, reducing reliance on costly annotated datasets.

## Contribution

It presents a novel neural approach using posterior regularization to incorporate lexicon and unlabeled data for Chinese word segmentation.

## Key findings

- Effective on multiple benchmark datasets
- Works well in in-domain and cross-domain scenarios
- Reduces need for extensive labeled data

## Abstract

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.