图谱构建小组 - Revision history

望尽天涯路 at 11:00, 11 November 2020

2020-11-11T11:00:05Z

望尽天涯路 at 10:59, 11 November 2020

2020-11-11T10:59:55Z

望尽天涯路 at 10:59, 11 November 2020

2020-11-11T10:59:39Z

望尽天涯路 at 10:58, 11 November 2020

2020-11-11T10:58:12Z

望尽天涯路 at 10:57, 11 November 2020

2020-11-11T10:57:55Z

望尽天涯路 at 10:54, 11 November 2020

2020-11-11T10:54:15Z

千里落花风 at 16:54, 10 November 2020

2020-11-10T16:54:36Z

千里落花风 at 16:54, 10 November 2020

2020-11-10T16:54:16Z

千里落花风 at 16:52, 10 November 2020

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千里落花风 at 16:50, 10 November 2020

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 # 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]

← Older revision		Revision as of 10:59, 11 November 2020
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	# 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]		# 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]

−	== ~~预训练模型~~ ==	+	== 预训练 ==
		+	# 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]

← Older revision		Revision as of 10:59, 11 November 2020
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	# 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]		# 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]		# 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]
		+
		+	== 预训练模型 ==

@@ Line 1: / Line 1: @@
 == 综述 ==
 # 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]
@@ Line 6: / Line 5: @@
 # 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  ==
+== 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]

← Older revision		Revision as of 10:57, 11 November 2020
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		+	</big>
	== 综述 ==		== 综述 ==
	# 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]		# 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]

← Older revision		Revision as of 10:54, 11 November 2020
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		+
	== 综述 ==		== 综述 ==
	# 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]		# 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]

@@ Line 1: / Line 1: @@
 == 综述 ==
-# 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]
+# 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]
-# 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]
+# 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]
+# 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]
+# 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]
+# 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]
+# 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]
+# 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]
+# 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]
+# 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]
+# 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]
-# 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]
+# 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]