Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
《Leveraging Knowledge Bases in LSTMs for Improving Machine Reading》 这篇文章是发表在2017年ACL上的，主要是聚焦于外部知识改善LSTM，运用在实体抽取和事件抽取任务。在ACE2005的数据集上得到了SOTA效果。 首先介绍这篇文章的两个知识库，一个是WordNet一个是NELL。 Word net是人工创造的一个词典，里面含有
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相关文章
- 1. 论文浅尝 | Leveraging Knowledge Bases in LSTMs
- 2. 【论文笔记】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC
- 3. Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
- 4. 【论文笔记】Improving Machine Reading Comprehension with General Reading Strategies（2019，NAACL）
- 5. [Paper Reading]--Exploiting Relevance Feedback in Knowledge Graph
- 6. Statistical Machine Translation Tutorial Reading
- 7. Enriching Knowledge Bases with Counting Quantifiers理解
- 8. NEURAL QUESTION REQUIREMENT INSPECTOR FOR ANSWERABILITY PREDICTION IN MACHINE READING COMPREHENSION
- 9. 论文《Adversarial Reading Networks For Machine Comprehension》
- 10. Machine Learning Basic Knowledge