NLP 论文汇总

纠错

1. Shulin Liu, Tao Yang, Tianchi Yue, FengZhang, Di Wang PLOME: Pre-training with Misspelled Knowledge for ChineseSpelling Correction[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics.2021
2. Zhang S , Huang H , Liu J , et al. Spelling Error Correction with Soft-Masked BERT[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020.

文本摘要

1. Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al. Recursively Summarizing Books with Human Feedback. OpenAI, 2021.

命名实体识别

1. Huang Z , Wei X , Kai Y . Bidirectional LSTM-CRF Models for Sequence Tagging[J]. Computer Science, 2015.
2. Lample G , Ballesteros M , Subramanian S , et al. Neural Architectures for Named Entity Recognition[C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.
3. Chiu J , Nichols E . Named Entity Recognition with Bidirectional LSTM-CNNs[J]. Computer Science, 2015.
4. Ma X , Hovy E . End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF[J]. 2016.
5. Yang J , Zhang Y . NCRF++: An Open-source Neural Sequence Labeling Toolkit[C]// 2018.
6. Zhang Y , Yang J . Chinese NER Using Lattice LSTM[J]. 2018.
7. Peng M , Ma R , Zhang Q , et al. Simplify the Usage of Lexicon in Chinese NER[J]. 2019.

Sequence Labeling

1. Akbik A , D Blythe, Vollgraf R . Contextual String Embeddings for Sequence Labeling. 2018.

机器翻译

1. Cho K , Merrienboer B V , Gulcehre C , et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation[J]. Computer Science, 2014.
2. Attention Is All You Need[J]. arXiv, 2017.

GIS

1. Lei Z , Lam N , Shams S , et al. Social and geographical disparities in Twitter use during Hurricane Harvey[J]. International Journal of Digital Earth, 2018, 12(1):1-19.
2. Wang J , Y Hu, Joseph K . NeuroTPR: A neuro‐net toponym recognition model for extracting locations from social media messages[J]. Transactions in GIS, 2020(11).

词嵌入/Pre-train language representation model

1. DE Rumelhart, Hinton G E , Williams R J . Learning Representations by Back Propagating Errors[J]. Nature, 1986, 323(6088):533-536.
2. Bengio Y , Réjean Ducharme, Vincent P , et al. A Neural Probabilistic Language Model.[J]. Journal of Machine Learning Research, 2003.
3. Mikolov T , Chen K , Corrado G , et al. Efficient Estimation of Word Representations in Vector Space[J]. Computer Science, 2013.
4. Mikolov T , Sutskever I , Kai C , et al. Distributed Representations of Words and Phrases and their Compositionality[J]. Advances in neural information processing systems, 2013, 26.
5. Le Q V , Mikolov T . Distributed Representations of Sentences and Documents. JMLR.org, 2014.
6. Goldberg Y , Levy O . word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method[J]. arXiv, 2014.
7. Rong X . word2vec Parameter Learning Explained[J]. Computer Science, 2014.
8. Pennington J , Socher R , Manning C . Glove: Global Vectors for Word Representation[C]// Conference on Empirical Methods in Natural Language Processing. 2014.
9. Devlin J , Chang M W , Lee K , et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.
10. Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training.
11. Peters M , Neumann M , Iyyer M , et al. Deep Contextualized Word Representations[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
12. Howard J , Ruder S . Universal Language Model Fine-tuning for Text Classification[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.
13. Zhang Z , Han X , Liu Z , et al. ERNIE: Enhanced Language Representation with Informative Entities[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.

语言模型

1. Mikolov T . Language Modeling for Spech Recognition in Czech. 2007.

文本匹配

1. Huang P S , He X , Gao J , et al. Learning deep structured semantic models for web search using clickthrough data[C]// Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013.
2. Neculoiu P , Versteegh M , Rotaru M . Learning Text Similarity with Siamese Recurrent Networks[C]// Repl4NLP workshop at ACL2016. 2016.
3. Wang S , Jing J . A Compare-Aggregate Model for Matching Text Sequences[J]. 2016.
4. Chen Q , Zhu X , Ling Z , et al. Enhanced LSTM for Natural Language Inference[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
5. Wang Z , Hamza W , Florian R . Bilateral Multi-Perspective Matching for Natural Language Sentences[J]. 2017.
6. Yi Tay†∗, Luu Anh Tuanψ∗, Siu Cheung Huiφ. Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
7. Pan B , Yang Y , Zhao Z , et al. Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference[J]. 2019.
8. Kim S , Kang I , Kwak N . Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information[J]. 2018.
9. Zhang K , Lv G , Wang L , et al. DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33:7442-7449.
10. Liu X , He P , Chen W , et al. Multi-Task Deep Neural Networks for Natural Language Understanding[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.