A Neural Network Approach to Verb Phrase Ellipsis Resolution


  • Wei-Nan Zhang Harbin Institute of Technology
  • Yue Zhang Westlake University
  • Yuanxing Liu Harbin Institute of Technology
  • Donglin Di Harbin Institute of Technology
  • Ting Liu Harbin Institute of Technology




Verb Phrase Ellipsis (VPE) is a linguistic phenomenon, where some verb phrases as syntactic constituents are omitted and typically referred by an auxiliary verb. It is ubiquitous in both formal and informal text, such as news articles and dialogues. Previous work on VPE resolution mainly focused on manually constructing features extracted from auxiliary verbs, syntactic trees, etc. However, the optimization of feature representation, the effectiveness of continuous features and the automatic composition of features are not well addressed. In this paper, we explore the advantages of neural models on VPE resolution in both pipeline and end-to-end processes, comparing the differences between statistical and neural models. Two neural models, namely multi-layer perception and the Transformer, are employed for the subtasks of VPE detection and resolution. Experimental results show that the neural models outperform the state-of-the-art baselines in both subtasks and the end-to-end results.




How to Cite

Zhang, W.-N., Zhang, Y., Liu, Y., Di, D., & Liu, T. (2019). A Neural Network Approach to Verb Phrase Ellipsis Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7468-7475. https://doi.org/10.1609/aaai.v33i01.33017468



AAAI Technical Track: Natural Language Processing