Neural Ranking Models for Temporal Dependency Structure Parsing

@inproceedings{Zhang2018NeuralRM,
  title={Neural Ranking Models for Temporal Dependency Structure Parsing},
  author={Yuchen Zhang and Nianwen Xue},
  booktitle={EMNLP},
  year={2018}
}
We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very… CONTINUE READING

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