Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification
這篇是2017年EMNLP收錄的論文。 本文提出了一種新的實體分類(EC)和關係抽取(RE)的方法。如果對BiLSTM+crf有一定了解的話理解這篇論文就容易得多，因爲他的整體思想是基於BiLSTM+crf的。 作者提出關係抽取對命名實體的識別有很大的作用，比如可以消除歧義問題，如mercedes可以是person，location，organization，但當提取出關係（live-in）後，就
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相關文章
- 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. [轉] 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