Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
This work improves Chinese word segmentation for NLP applications, but it is incremental as it builds on existing character-based neural models.
The paper tackled Chinese word segmentation by addressing weaknesses in existing neural models, such as reliance on manual bigram features and lack of word information, resulting in state-of-the-art performance of 96.5% on PKU and 98.0% on MSR datasets with word embeddings.
Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heavily rely on manually designed bigram feature, i.e. they are not good at capturing n-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich n-gram features without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineering, the model obtains competitive performance -- 95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves state-of-the-art performance on both datasets -- 96.5% on PKU and 98.0% on MSR, without using any external labeled resource.