“Reading table”版本间的差异
来自cslt Wiki
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| Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. AAAI'15. [http://nlp.csai.tsinghua.edu.cn/~lzy/publications/aaai2015_transr.pdf pdf][https://github.com/mrlyk423/relation_extraction code] | | Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. AAAI'15. [http://nlp.csai.tsinghua.edu.cn/~lzy/publications/aaai2015_transr.pdf pdf][https://github.com/mrlyk423/relation_extraction code] | ||
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+ | |rowspan=1|2015/07/10 || rowspan='1'| || Context-Dependent Translation Selection Using Convolutional Neural Network [http://arxiv.org/abs/1503.02357] | ||
+ | |Syntax-based Deep Matching of Short Texts [http://arxiv.org/abs/1503.02427] | ||
+ | Convolutional Neural Network Architectures for Matching Natural Language Sentences[http://www.hangli-hl.com/uploads/3/1/6/8/3168008/hu-etal-nips2014.pdf] | ||
+ | LSTM: A Search Space Odyssey [http://arxiv.org/pdf/1503.04069.pdf] | ||
+ | A Deep Embedding Model for Co-occurrence Learning [http://arxiv.org/abs/1504.02824] | ||
+ | Text segmentation based on semantic word embeddings[http://arxiv.org/abs/1503.05543] | ||
+ | semantic parsing via paraphrashings[http://www.cs.tau.ac.il/research/jonathan.berant/homepage_files/publications/ACL14.pdf] | ||
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2015年7月10日 (五) 06:42的版本
Date | Speaker | Materials | |
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2014/10/22 | Zhang Dong Xu | Why RNN? PPT paper 1,paper 2 | |
2014/12/8 | Liu Rong | Yu Zhao, Zhiyuan Liu, Maosong Sun. Phrase Type Sensitive Tensor Indexing Model for Semantic Composition. AAAI'15. pdf | |
Yang Liu, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun. Topical Word Embeddings. AAAI'15. pdfcode | |||
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. AAAI'15. pdfcode | |||
2015/07/10 | Context-Dependent Translation Selection Using Convolutional Neural Network [1] | Syntax-based Deep Matching of Short Texts [2]
Convolutional Neural Network Architectures for Matching Natural Language Sentences[3] LSTM: A Search Space Odyssey [4] A Deep Embedding Model for Co-occurrence Learning [5] Text segmentation based on semantic word embeddings[6] semantic parsing via paraphrashings[7] |