语义角色标记：Semantic Role Labeling

本文说的是，在一个句子中，已经知道一个谓语，如何从这个句子中抽取他的主语，宾语的故事，江湖人称：语义角色标记，这里的主语和宾语就是已知的谓语的主语和宾语。这里作者把问题变成了一个序列标记的问题，就是预测每个单词是不是主语/宾语的词语的问题。作者是华盛顿大学的美女学霸LuhengHe，现在已经在脸家当码农了。

---

---

Deep semantic role labeling: What works and what’s next

He, Luheng, Kenton Lee, Mike Lewis, and Luke Zettlemoyer.

In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 473-483. 2017.

---

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on the CoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.