- Deep Learning (I.Goodfellow, 2016)
- 기계 학습 (오일석, 2017.12)
Review Papers
- A Primer on Neural Network Models for Natural Language Processing (Y.Goldberg, 2015.10)
- A Critical Review of Recurrent Neural Networks for Sequence Learning (ZC.Lipton, 2015.05)
Various kinds of Deep Learning Models
- Neural machine translation by jointly learning to align and translate (D.Bahdanau, ICLR, 2014)
- Sequence to Sequence Learning with Neural Networks (I.Sutskever, NIPS, 2014)
- Going Deeper with Convolutions (C.Szegedy, 2014)
- Playing Atari with Deep Reinforcement Learning (V.Mnih, 2013)
- Generating Text with Recurrent Neural Networks (I.Sutskever, 2011)
Understanding Deep Learning Models
- Visualizing and Understanding Recurrent Networks (A.Karpathy, 2015.11)
- Visualizing and Understanding Convolutional Networks (MD.Jeiler, 2014)
Probabilistic Graphical Models
- Application of Deep Belief Networks for Natural Language Understanding (R.Sarikaya, 2014)
- Latent Dirichlet Allocation (MD.Blei, JMLR, 2003)
Deep Learning with Memory(Attention)
- Attention Is All You Need (A.Vaswani, NIPS 2017)
- Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (A.Kumar, ICML 2016)
- Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems (C.Raffel, ICLR(workshop) 2016)
- Neural Machine Translation by Jointly Learning to Align and Translate (D.Bahdanau, ICLR 2015)
- End-To-End Memory Networks (S.Sukhbaatar, NIPS 2015)
Text Classification with Deep Learning Models
- Hierarchical Attention Networks for Document Classification (Z.Yang, NAACL 2016)
- Recurrent Convolutional Neural Networks for Text Classification (L.Siwei, AAAI 2015)
- Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014)
Distributed Representations for Language
- Enriching Word Vectors with Subword Information (P.Bojanowski, 2016)
- Glove: Global vectors for word representation (J.Pennington, 2014.10)
- Distributed Representations of Sentences and Documents (QV.Le, 2014.05)
- Distributed Representations of Words and Phrases and their Compositionality (T.Mikolov, 2013.10)
- Efficient Estimation of Word Representations in Vector Space (T.Mikolov, 2013.09)
- Linguistic Regularities in Continuous Space Word Representations (T.Mikolov, 2013.06)
- Recurrent Neural Network based Language Model (T.Mikolov, 2010)
- A Neural Probabilistic Language Model (Y.Bengio, 2003)
- Indexing by Latent Semantic Analysis (S.Deerwester, 1990)
Named Entity Recognition (CoNLL2003)
- Named Entity Recognition with stack residual LSTM and trainable bias decoding (Q.Tran, 2017.07) (91.69)
- Semi-supervised Sequence Tagging with Bidirectional Language Models (ME.Peters, 2017.04) (91.93)
- Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks (Z.Yang, 2017.03) (91.26)
- End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF (X.Ma and E.Hovy, 2016.06) (91.21)
- Neural Architectures for Named Entity Recognition (G.Lample, 2016.04) (90.94)
- Named Entity Recognition with bidirectional LSTM-CNNs (JPC.Chiu, 2015.11) (91.62)
- Natural Language Processing (almost) from Scratch (R.Collobert, 2011)
- A Survey of Named Entity Recognition and Classification (2007.01)
ETC.
- Tweet Segmentation and Its Application to Named Entity Recognition (C Li, 2015)
- An Empirical Study of Semantic Similarity in WordNet and Word2Vec (2014)
- Building Bridges for Web Query Classification (D Shen, 2006)
- The Probable Error of a Mean (Student, 1908)
- Jumping NLP Curves (E.Cambria, 2014.04)
- Calculus on Computational Graphs: Backpropagation (Aug 31, 2015, Colah)
- Understanding LSTM Networks (Aug 27, 2015, Colah)
- Deep Learning and NLP and Representations (July 7, 2014, Colah)
- Attention and Memory in Deep Learning and NLP (Jan 3, 2016, WILDML)
- Understanding Convolutional Neural Networks for NLP (Nov 7, 2015, WILDML)
- 프로그래머를 위한 알파고 (Mar 13, 2016, Slideshare)