Ji Wen


2018

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Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
Ji Wen | Xu Sun | Xuancheng Ren | Qi Su
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model. Experimental results show that the proposed method significantly improves the F1 score by 10.3, and outperforms the state-of-the-art approaches on Chinese literature text.

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Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Xuancheng Ren | Xu Sun | Ji Wen | Bingzhen Wei | Weidong Zhan | Zhiyuan Zhang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)