Paper: Max-Margin Tensor Neural Network for Chinese Word Segmentation

ACL ID P14-1028
Title Max-Margin Tensor Neural Network for Chinese Word Segmentation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Recently, neural network models for nat- ural language processing tasks have been increasingly focused on for their ability to alleviate the burden of manual feature engineering. In this paper, we propose a novel neural network model for Chinese word segmentation called Max-Margin Tensor Neural Network (MMTNN). By exploiting tag embeddings and tensor- based transformation, MMTNN has the ability to model complicated interactions between tags and context characters. Fur- thermore, a new tensor factorization ap- proach is proposed to speed up the model and avoid overfitting. Experiments on the benchmark dataset show that our model achieves better performances than previ- ous neural network models and that our model can achieve a competitive perfor- mance with minimal feature engineering. Desp...