Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
这篇是2017年EMNLP收录的论文。 本文提出了一种新的实体分类(EC)和关系抽取(RE)的方法。如果对BiLSTM+crf有一定了解的话理解这篇论文就容易得多，因为他的整体思想是基于BiLSTM+crf的。 作者提出关系抽取对命名实体的识别有很大的作用，比如可以消除歧义问题，如mercedes可以是person，location，organization，但当提取出关系（live-in）后，就
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相关文章
- 1. [EMNLP2017]Global Normalization of Convolutional Neural Networks for Joint Entity and Relation
- 2. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 3. Recurrent Convolutional Neural Networks for Text Classification
- 4. Appearance-and-Relation Networks for Video Classification
- 5. Convolutional Neural Networks for Sentence Classification
- 6. Understanding Convolutional Neural Networks for NLP
- 7. [转] Understanding Convolutional Neural Networks for NLP
- 8. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
- 9. 《Convolutional Neural Networks for Sentence Classification》
- 10. How to Use Convolutional Neural Networks for Time Series Classification