Difference between revisions of "图谱构建小组"

From dbgroup
Jump to: navigation, search
 
(10 intermediate revisions by 2 users not shown)
Line 1: Line 1:
−
'''Survey papers'''</br>
+
 
−
# 刘峤, 李杨, 段宏,等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600.</br>
+
== 综述 ==
−
# 徐增林, 盛泳潘, 贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606.</br>
+
# 刘峤, 李杨, 段宏,等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600.[http://crad.ict.ac.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=3127 paper]
−
# Wang Q , Mao Z , Wang B , et al. Knowledge Graph Embedding: A Survey of Approaches and Applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, PP(99):1-1.</br>
+
# 徐增林, 盛泳潘, 贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606.[http://www.xml-data.org/dzkj-nature/html/201645589.htm paper]
 +
# 李舟军, 范宇, 吴贤杰. 面向自然语言处理的预训练技术研究综述[J]. 计算机科学, 2020, v.47(03):170-181.[http://www.cnki.com.cn/Article/CJFDTotal-JSJA202003028.htm paper]
 +
 
 +
== 预训练模型 ==
 +
# Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.[https://arxiv.org/pdf/1508.01991.pdf paper]
 +
# Vaswani A , Shazeer N , Parmar N , et al. Attention Is All You Need[J]. arXiv, 2017.[https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf paper]
 +
# Devlin J , Chang M W , Lee K , et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.[https://arxiv.org/pdf/1810.04805.pdf paper]
 +
# Liu Y , Ott M , Goyal N , et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[J]. 2019.[https://arxiv.org/pdf/1907.11692.pdf paper]
 +
# Sun Y, Wang S, Li Y, et al. Ernie: Enhanced representation through knowledge integration[J]. arXiv preprint arXiv:1904.09223, 2019.[https://arxiv.org/pdf/1904.09223.pdf paper]
 +
# Zhang Z, Han X, Liu Z, et al. ERNIE: Enhanced language representation with informative entities[J]. arXiv preprint arXiv:1905.07129, 2019.[https://arxiv.org/pdf/1905.07129.pdf paper]
 +
# Sun Y, Wang S, Li Y K, et al. ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding[C]//AAAI. 2020: 8968-8975.[https://arxiv.org/pdf/1907.12412.pdf?source=post_page paper]
 +
# Cui Y, Che W, Liu T, et al. Pre-training with whole word masking for chinese bert[J]. arXiv preprint arXiv:1906.08101, 2019.[https://arxiv.org/pdf/1906.08101.pdf paper]
 +
# Jiao X, Yin Y, Shang L, et al. Tinybert: Distilling bert for natural language understanding[J]. arXiv preprint arXiv:1909.10351, 2019.[https://arxiv.org/pdf/1909.10351.pdf paper]
 +
# Yang Z , Dai Z , Yang Y , et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding[J]. 2019.[https://papers.nips.cc/paper/2019/file/dc6a7e655d7e5840e66733e9ee67cc69-Paper.pdf paper]

Latest revision as of 11:00, 11 November 2020

综述

  1. 刘峤, 李杨, 段宏,等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600.paper
  2. 徐增林, 盛泳潘, 贺丽荣,等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606.paper
  3. 李舟军, 范宇, 吴贤杰. 面向自然语言处理的预训练技术研究综述[J]. 计算机科学, 2020, v.47(03):170-181.paper

预训练模型

  1. Huang Z, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.paper
  2. Vaswani A , Shazeer N , Parmar N , et al. Attention Is All You Need[J]. arXiv, 2017.paper
  3. Devlin J , Chang M W , Lee K , et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.paper
  4. Liu Y , Ott M , Goyal N , et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[J]. 2019.paper
  5. Sun Y, Wang S, Li Y, et al. Ernie: Enhanced representation through knowledge integration[J]. arXiv preprint arXiv:1904.09223, 2019.paper
  6. Zhang Z, Han X, Liu Z, et al. ERNIE: Enhanced language representation with informative entities[J]. arXiv preprint arXiv:1905.07129, 2019.paper
  7. Sun Y, Wang S, Li Y K, et al. ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding[C]//AAAI. 2020: 8968-8975.paper
  8. Cui Y, Che W, Liu T, et al. Pre-training with whole word masking for chinese bert[J]. arXiv preprint arXiv:1906.08101, 2019.paper
  9. Jiao X, Yin Y, Shang L, et al. Tinybert: Distilling bert for natural language understanding[J]. arXiv preprint arXiv:1909.10351, 2019.paper
  10. Yang Z , Dai Z , Yang Y , et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding[J]. 2019.paper
Retrieved from "http://www.tjudb.cn/dbgroup/index.php?title=图谱构建小组&oldid=649"