Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
《Leveraging Knowledge Bases in LSTMs for Improving Machine Reading》 這篇文章是發表在2017年ACL上的，主要是聚焦於外部知識改善LSTM，運用在實體抽取和事件抽取任務。在ACE2005的數據集上得到了SOTA效果。 首先介紹這篇文章的兩個知識庫，一個是WordNet一個是NELL。 Word net是人工創造的一個詞典，裏面含有
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相關文章
- 1. 論文淺嘗 | Leveraging Knowledge Bases in LSTMs
- 2. 【論文筆記】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. 【論文筆記】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. 論文《Adversarial Reading Networks For Machine Comprehension》
- 10. Machine Learning Basic Knowledge