Jiang Bian


2019

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Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Shun Zheng | Wei Cao | Wei Xu | Jiang Bian
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Most existing event extraction (EE) methods merely extract event arguments within the sentence scope. However, such sentence-level EE methods struggle to handle soaring amounts of documents from emerging applications, such as finance, legislation, health, etc., where event arguments always scatter across different sentences, and even multiple such event mentions frequently co-exist in the same document. To address these challenges, we propose a novel end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic graph to fulfill the document-level EE (DEE) effectively. Moreover, we reformalize a DEE task with the no-trigger-words design to ease the document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we build a large-scale real-world dataset consisting of Chinese financial announcements with the challenges mentioned above. Extensive experiments with comprehensive analyses illustrate the superiority of Doc2EDAG over state-of-the-art methods. Data and codes can be found at https://github.com/dolphin-zs/Doc2EDAG.

2016

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Solving Verbal Questions in IQ Test by Knowledge-Powered Word Embedding
Huazheng Wang | Fei Tian | Bin Gao | Chengjieren Zhu | Jiang Bian | Tie-Yan Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2014

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Co-learning of Word Representations and Morpheme Representations
Siyu Qiu | Qing Cui | Jiang Bian | Bin Gao | Tie-Yan Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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A Probabilistic Model for Learning Multi-Prototype Word Embeddings
Fei Tian | Hanjun Dai | Jiang Bian | Bin Gao | Rui Zhang | Enhong Chen | Tie-Yan Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers