update

readme/nlp/anaphora_parser.md

@@ -2,9 +2,9 @@
 
 ## Coreference and Anaphora Resolution
 - [2012 EMNLP] **Joint Entity and Event Coreference Resolution across Documents**, [[paper]](https://aclweb.org/anthology/D/D12/D12-1045.pdf).
-- [2016 EMNLP] **Deep Reinforcement Learning for Mention-Ranking Coreference Models**, [[paper]](https://arxiv.org/abs/1609.08667), [[blog]](https://medium.com/huggingface/state-of-the-art-neural-coreference-resolution-for-chatbots-3302365dcf30), [[demo]](https://huggingface.co/coref/), sources: [[huggingface/neuralcoref]](https://github.com/huggingface/neuralcoref), [[clarkkev/deep-coref]](https://github.com/clarkkev/deep-coref).
+- [2016 EMNLP] **Deep Reinforcement Learning for Mention-Ranking Coreference Models**, [[paper]](https://arxiv.org/pdf/1609.08667.pdf), [[blog]](https://medium.com/huggingface/state-of-the-art-neural-coreference-resolution-for-chatbots-3302365dcf30), [[demo]](https://huggingface.co/coref/), sources: [[huggingface/neuralcoref]](https://github.com/huggingface/neuralcoref), [[clarkkev/deep-coref]](https://github.com/clarkkev/deep-coref).
 - [2016 ACL] **Improving Coreference Resolution by Learning Entity-Level Distributed Representations**, [[paper]](https://cs.stanford.edu/people/kevclark/resources/clark-manning-acl16-improving.pdf), sources: [[clarkkev/deep-coref]](https://github.com/clarkkev/deep-coref).
-- [2017 ArXiv] **Linguistic Knowledge as Memory for Recurrent Neural Networks**, [[paper]](https://arxiv.org/abs/1703.02620).
+- [2017 ArXiv] **Linguistic Knowledge as Memory for Recurrent Neural Networks**, [[paper]](https://arxiv.org/pdf/1703.02620.pdf).
 
 ## Dependency Parser
 - [2014 EMNLP] **A Fast and Accurate Dependency Parser using Neural Networks**, [[paper]](https://cs.stanford.edu/~danqi/papers/emnlp2014.pdf), sources: [[akjindal53244/dependency_parsing_tf]](https://github.com/akjindal53244/dependency_parsing_tf), [[ljj314zz/dependency_parsing_tf-master]](https://github.com/ljj314zz/dependency_parsing_tf-master).
@@ -17,4 +17,4 @@
 
 ## Grammatical Error Correction
 - [2014 CoNLL] **The CoNLL-2014 Shared Task on Grammatical Error Correction**, [[paper]](http://www.aclweb.org/anthology/W14-1701), [[homepage]](http://www.comp.nus.edu.sg/~nlp/conll14st.html).
-- [2018 AAAI] **A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction**, [[paper]](https://arxiv.org/abs/1801.08831), [[nusnlp/mlconvgec2018]](https://github.com/nusnlp/mlconvgec2018).
+- [2018 AAAI] **A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction**, [[paper]](https://arxiv.org/pdf/1801.08831.pdf), [[nusnlp/mlconvgec2018]](https://github.com/nusnlp/mlconvgec2018).

readme/nlp/commonsense.md

@@ -4,15 +4,15 @@
 - [2013 NIPS] **Reasoning With Neural Tensor Networks for Knowledge Base Completion**, [[paper]](https://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pdf), sources: [[siddharth-agrawal/Neural-Tensor-Network]](https://github.com/siddharth-agrawal/Neural-Tensor-Network), [[dddoss/tensorflow-socher-ntn]](https://github.com/dddoss/tensorflow-socher-ntn).
 - [2013 NIPS] **TransE: Translating Embeddings for Modeling Multi-relational Data**, [[paper]](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
 - [2014 AAAI] **TransH: Knowledge Graph Embedding by Translating on Hyperplanes**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8531/8546), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
-- [2015 EMNLP] **PTransE: Modeling Relation Paths for Representation Learning of Knowledge Bases**, [[paper]](https://arxiv.org/abs/1506.00379), [[homepage]](https://github.com/thunlp), sources: [[thunlp/Fast-TransX]](https://github.com/thunlp/Fast-TransX).
+- [2015 EMNLP] **PTransE: Modeling Relation Paths for Representation Learning of Knowledge Bases**, [[paper]](https://arxiv.org/pdf/1506.00379.pdf), [[homepage]](https://github.com/thunlp), sources: [[thunlp/Fast-TransX]](https://github.com/thunlp/Fast-TransX).
 - [2015 AAAI] **TransR: Learning Entity and Relation Embeddings for Knowledge Graph Completion**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9571/9523), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
 - [2015 ACL] **TransD: Knowledge Graph Embedding via Dynamic Mapping Matrix**, [[paper]](http://www.aclweb.org/anthology/P15-1067), sources: [[thunlp/TensorFlow-TransX]](https://github.com/thunlp/TensorFlow-TransX).
 - [2016 AAAI] **Knowledge Graph Completion with Adaptive Sparse Transfer Matrix**, [[paper]](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11982/11693), sources: [[FrankWork/transparse]](https://github.com/FrankWork/transparse), [[thunlp/Fast-TransX]](https://github.com/thunlp/Fast-TransX).
 - [2016 ACL] **Commonsense Knowledge Base Completion**, [[paper]](http://ttic.uchicago.edu/~kgimpel/papers/li+etal.acl16.pdf), [[homepage]](http://ttic.uchicago.edu/~kgimpel/commonsense.html), sources: [[Lorraine333/ACL_CKBC]](https://github.com/Lorraine333/ACL_CKBC).
-- [2017 AKBC] **RelNet: End-to-End Modeling of Entities & Relations**, [[paper]](https://arxiv.org/abs/1706.07179), [[homepage]](http://thetb.github.io).
+- [2017 AKBC] **RelNet: End-to-End Modeling of Entities & Relations**, [[paper]](https://arxiv.org/pdf/1706.07179.pdf), [[homepage]](http://thetb.github.io).
 - [2017 EMNLP] **Context-Aware Representations for Knowledge Base Relation Extraction**, [[paper]](http://aclweb.org/anthology/D17-1188), sources: [[UKPLab/emnlp2017-relation-extraction]](https://github.com/UKPLab/emnlp2017-relation-extraction).
 - [2018 AAAI] **SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings**, [[paper]](http://sentic.net/senticnet-5.pdf).
 
 ## Commonsense Knowledge Base and Its Usages
-- [2017 AAAI] **ConceptNet 5.5: An Open Multilingual Graph of General Knowledge**, [[paper]](https://arxiv.org/abs/1612.03975), sources: [[GitHub page]](https://github.com/commonsense), [[commonsense/conceptnet5]](https://github.com/commonsense/conceptnet5), [[commonsense/conceptnet-numberbatch]](https://github.com/commonsense/conceptnet-numberbatch).
+- [2017 AAAI] **ConceptNet 5.5: An Open Multilingual Graph of General Knowledge**, [[paper]](https://arxiv.org/pdf/1612.03975.pdf), sources: [[GitHub page]](https://github.com/commonsense), [[commonsense/conceptnet5]](https://github.com/commonsense/conceptnet5), [[commonsense/conceptnet-numberbatch]](https://github.com/commonsense/conceptnet-numberbatch).
 - [2017 CIKM] **Commonsense for Machine Intelligence: Text to Knowledge and Knowledge to Text**, [[slides]](http://people.mpi-inf.mpg.de/~ntandon/presentations/cikm-2017-tutorial-commonsense/commonsense.pdf), [[CIKM 2017 Singapore Tutorials]](http://cikm2017.org/tutorialmain.html), [[Commonsense for Machine Intelligence, Allen Institute, CIKM 2017 TUTORIAL]](http://allenai.org/tutorials/csk/), [[Allen Institute]](http://allenai.org/index.html).

readme/nlp/dialogue_system.md

@@ -1,15 +1,15 @@
 # Dialogue / Conversation / Chatbot System
 
 - [2013 IEEE] **POMDP-based Statistical Spoken Dialogue Systems: a Review**, [[paper]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/young2013procieee.pdf).
-- [2014 NIPS] **Sequence to Sequence Learning with Neural Networks**, [[paper]](https://arxiv.org/abs/1409.3215), sources: [[farizrahman4u/seq2seq]](https://github.com/farizrahman4u/seq2seq), [[ma2rten/seq2seq]](https://github.com/ma2rten/seq2seq), [[JayParks/tf-seq2seq]](https://github.com/JayParks/tf-seq2seq), [[macournoyer/neuralconvo]](https://github.com/macournoyer/neuralconvo).
-- [2015 CIKM] **A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion**, [[paper]](https://arxiv.org/abs/1507.02221.pdf), sources: [[sordonia/hred-qs]](https://github.com/sordonia/hred-qs).
-- [2015 EMNLP] **Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems**, [[paper]](https://arxiv.org/abs/1508.01745), sources: [[shawnwun/RNNLG]](https://github.com/shawnwun/RNNLG), [[hit-computer/SC-LSTM]](https://github.com/hit-computer/SC-LSTM).
-- [2015 ArXiv] **Attention with Intention for a Neural Network Conversation Model**, [[paper]](https://arxiv.org/abs/1510.08565).
-- [2015 ACL] **Neural Responding Machine for Short-Text Conversation**, [[paper]](https://arxiv.org/abs/1503.02364).
-- [2016 AAAI] **Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**, [[paper]](https://arxiv.org/abs/1507.04808), sources: [[suriyadeepan/augmented_seq2seq]](https://github.com/suriyadeepan/augmented_seq2seq), [[julianser/hed-dlg]](https://github.com/julianser/hed-dlg), [[sordonia/hed-dlg]](https://github.com/sordonia/hed-dlg), [[julianser/hred-latent-piecewise]](https://github.com/julianser/hred-latent-piecewise), [[julianser/hed-dlg-truncated]](https://github.com/julianser/hed-dlg-truncated).
-- [2016 ACL] **On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems**, [[paper]](https://arxiv.org/abs/1605.07669).
-- [2016 EMNLP] **Deep Reinforcement Learning for Dialogue Generation**, [[paper]](https://arxiv.org/abs/1606.01541), sources: [[liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow]](https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow).
+- [2014 NIPS] **Sequence to Sequence Learning with Neural Networks**, [[paper]](https://arxiv.org/pdf/1409.3215.pdf), sources: [[farizrahman4u/seq2seq]](https://github.com/farizrahman4u/seq2seq), [[ma2rten/seq2seq]](https://github.com/ma2rten/seq2seq), [[JayParks/tf-seq2seq]](https://github.com/JayParks/tf-seq2seq), [[macournoyer/neuralconvo]](https://github.com/macournoyer/neuralconvo).
+- [2015 CIKM] **A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion**, [[paper]](https://arxiv.org/pdf/1507.02221.pdf), sources: [[sordonia/hred-qs]](https://github.com/sordonia/hred-qs).
+- [2015 EMNLP] **Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems**, [[paper]](https://arxiv.org/pdf/1508.01745.pdf), sources: [[shawnwun/RNNLG]](https://github.com/shawnwun/RNNLG), [[hit-computer/SC-LSTM]](https://github.com/hit-computer/SC-LSTM).
+- [2015 ArXiv] **Attention with Intention for a Neural Network Conversation Model**, [[paper]](https://arxiv.org/pdf/1510.08565.pdf).
+- [2015 ACL] **Neural Responding Machine for Short-Text Conversation**, [[paper]](https://arxiv.org/pdf/1503.02364.pdf).
+- [2016 AAAI] **Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models**, [[paper]](https://arxiv.org/pdf/1507.04808.pdf), sources: [[suriyadeepan/augmented_seq2seq]](https://github.com/suriyadeepan/augmented_seq2seq), [[julianser/hed-dlg]](https://github.com/julianser/hed-dlg), [[sordonia/hed-dlg]](https://github.com/sordonia/hed-dlg), [[julianser/hred-latent-piecewise]](https://github.com/julianser/hred-latent-piecewise), [[julianser/hed-dlg-truncated]](https://github.com/julianser/hed-dlg-truncated).
+- [2016 ACL] **On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems**, [[paper]](https://arxiv.org/pdf/1605.07669.pdf).
+- [2016 EMNLP] **Deep Reinforcement Learning for Dialogue Generation**, [[paper]](https://arxiv.org/pdf/1606.01541.pdf), sources: [[liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow]](https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow).
 - [2016 EMNLP] **Multi-view Response Selection for Human-Computer Conversation**, [[paper]](http://www.aclweb.org/anthology/D16-1036).
-- [2017 ACM] **A Survey on Dialogue Systems: Recent Advances and New Frontiers**, [[paper]](https://arxiv.org/abs/1711.01731.pdf), sources: [[shawnspace/survey-in-dialog-system]](https://github.com/shawnspace/survey-in-dialog-system).
-- [2017 EMNLP] **Adversarial Learning for Neural Dialogue Generation**, [[paper]](https://arxiv.org/abs/1701.06547), sources: [[jiweil/Neural-Dialogue-Generation]](https://github.com/jiweil/Neural-Dialogue-Generation), [[liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow]](https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow).
-- [2017 ACL] **Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots**, [[paper]](https://arxiv.org/abs/1612.01627), sources: [[MarkWuNLP/MultiTurnResponseSelection]](https://github.com/MarkWuNLP/MultiTurnResponseSelection), [[krayush07/sequential-match-network]](https://github.com/krayush07/sequential-match-network).
+- [2017 ACM] **A Survey on Dialogue Systems: Recent Advances and New Frontiers**, [[paper]](https://arxiv.org/pdf/1711.01731.pdf), sources: [[shawnspace/survey-in-dialog-system]](https://github.com/shawnspace/survey-in-dialog-system).
+- [2017 EMNLP] **Adversarial Learning for Neural Dialogue Generation**, [[paper]](https://arxiv.org/pdf/1701.06547.pdf), sources: [[jiweil/Neural-Dialogue-Generation]](https://github.com/jiweil/Neural-Dialogue-Generation), [[liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow]](https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow).
+- [2017 ACL] **Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots**, [[paper]](https://arxiv.org/pdf/1612.01627.pdf), sources: [[MarkWuNLP/MultiTurnResponseSelection]](https://github.com/MarkWuNLP/MultiTurnResponseSelection), [[krayush07/sequential-match-network]](https://github.com/krayush07/sequential-match-network).

readme/nlp/embeddings.md

@@ -6,17 +6,17 @@
 ## Word Embedding
 - [2008 NIPS] **HLBL: A Scalable Hierarchical Distributed Language Model**, [[paper]](http://www.cs.toronto.edu/~fritz/absps/andriytree.pdf), [[wenjieguan/Log-bilinear-language-models]](https://github.com/wenjieguan/Log-bilinear-language-models).
 - [2010 INTERSPEECH] **RNNLM: Recurrent neural network based language model**, [[paper]](http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf), [[Ph.D. Thesis]](http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf), [[slides]](http://www.fit.vutbr.cz/~imikolov/rnnlm/google.pdf), sources: [[mspandit/rnnlm]](https://github.com/mspandit/rnnlm).
-- [2013 NIPS] **Word2Vec: Distributed Representations of Words and Phrases and their Compositionality**, [[paper]](https://arxiv.org/abs/1310.4546), [[word2vec explained]](https://arxiv.org/abs/1402.3722), [[params explained]](https://arxiv.org/abs/1411.2738), [[blog]](https://isaacchanghau.github.io/post/word2vec/), sources: [[word2vec]](https://code.google.com/archive/p/word2vec/), [[dav/word2vec]](https://github.com/dav/word2vec), [[yandex/faster-rnnlm]](https://github.com/yandex/faster-rnnlm), [[tf-word2vec]](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/word2vec), [[zake7749/word2vec-tutorial]](https://github.com/zake7749/word2vec-tutorial).
+- [2013 NIPS] **Word2Vec: Distributed Representations of Words and Phrases and their Compositionality**, [[paper]](https://arxiv.org/pdf/1310.4546.pdf), [[word2vec explained]](https://arxiv.org/pdf/1402.3722.pdf), [[params explained]](https://arxiv.org/pdf/1411.2738.pdf), [[blog]](https://isaacchanghau.github.io/post/word2vec/), sources: [[word2vec]](https://code.google.com/archive/p/word2vec/), [[dav/word2vec]](https://github.com/dav/word2vec), [[yandex/faster-rnnlm]](https://github.com/yandex/faster-rnnlm), [[tf-word2vec]](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/word2vec), [[zake7749/word2vec-tutorial]](https://github.com/zake7749/word2vec-tutorial).
 - [2013 CoNLL] **Better Word Representations with Recursive Neural Networks for Morphology**, [[paper]](https://nlp.stanford.edu/~lmthang/data/papers/conll13_morpho.pdf).
 - [2014 ACL] **Word2Vecf: Dependency-Based Word Embeddings**, [[paper]](http://www.aclweb.org/anthology/P14-2050), [[blog]](https://isaacchanghau.github.io/post/word2vecf/), sources: [[Yoav Goldberg/word2vecf]](https://bitbucket.org/yoavgo/word2vecf), [[IsaacChanghau/Word2VecfJava]](https://github.com/IsaacChanghau/Word2VecfJava).
 - [2014 EMNLP] **GloVe: Global Vectors for Word Representation**, [[paper]](https://nlp.stanford.edu/pubs/glove.pdf), [[homepage]](https://nlp.stanford.edu/projects/glove/), sources: [[stanfordnlp/GloVe]](https://github.com/stanfordnlp/GloVe).
 - [2014 ICML] **Compositional Morphology for Word Representations and Language Modelling**, [[paper]](http://proceedings.mlr.press/v32/botha14.pdf), sources: [[thompsonb/comp-morph]](https://github.com/thompsonb/comp-morph), [[claravania/subword-lstm-lm]](https://github.com/claravania/subword-lstm-lm).
 - [2015 ACL] **Hyperword: Improving Distributional Similarity with Lessons Learned from Word Embeddings**, [[paper]](http://www.aclweb.org/anthology/Q15-1016), sources: [[Omer Levy/hyperwords]](https://bitbucket.org/omerlevy/hyperwords).
-- [2016 ICLR] **Exploring the Limits of Language Modeling**, [[paper]](https://arxiv.org/abs/1602.02410.pdf), [[slides]](https://www.cs.toronto.edu/~duvenaud/courses/csc2541/slides/lipnet.pdf), sources: [[tensorflow/models/lm_1b]](https://github.com/tensorflow/models/tree/master/research/lm_1b).
+- [2016 ICLR] **Exploring the Limits of Language Modeling**, [[paper]](https://arxiv.org/pdf/1602.02410.pdf), [[slides]](https://www.cs.toronto.edu/~duvenaud/courses/csc2541/slides/lipnet.pdf), sources: [[tensorflow/models/lm_1b]](https://github.com/tensorflow/models/tree/master/research/lm_1b).
 - [2016 CoNLL] **Context2Vec: Learning Generic Context Embedding with Bidirectional LSTM**, [[paper]](http://www.aclweb.org/anthology/K16-1006), sources: [[orenmel/context2vec]](https://github.com/orenmel/context2vec).
-- [2016 IEEE Intelligent Systems] **How to Generate a Good Word Embedding?**, [[paper]](https://arxiv.org/abs/1507.05523), [[基于神经网络的词和文档语义向量表示方法研究]](https://arxiv.org/pdf/1611.05962.pdf), [[blog]](http://licstar.net/archives/620), sources: [[licstar/compare]](https://github.com/licstar/compare).
-- [2016 ArXiv] **Linear Algebraic Structure of Word Senses, with Applications to Polysemy**, [[paper]](https://arxiv.org/abs/1601.03764.pdf), [[slides]](https://pdfs.semanticscholar.org/d770/5adf01fc9791337ed17dd37236129ef3a0f4.pdf), sources: [[YingyuLiang/SemanticVector]](https://github.com/YingyuLiang/SemanticVector).
-- [2017 ACL] **FastText: Enriching Word Vectors with Subword Information**, [[paper]](https://arxiv.org/abs/1607.04606.pdf), sources: [[facebookresearch/fastText]](https://github.com/facebookresearch/fastText), [[salestock/fastText.py]](https://github.com/salestock/fastText.py).
+- [2016 IEEE Intelligent Systems] **How to Generate a Good Word Embedding?**, [[paper]](https://arxiv.org/pdf/1507.05523.pdf), [[基于神经网络的词和文档语义向量表示方法研究]](https://arxiv.org/pdf/1611.05962.pdf), [[blog]](http://licstar.net/archives/620), sources: [[licstar/compare]](https://github.com/licstar/compare).
+- [2016 ArXiv] **Linear Algebraic Structure of Word Senses, with Applications to Polysemy**, [[paper]](https://arxiv.org/pdf/1601.03764.pdf), [[slides]](https://pdfs.semanticscholar.org/d770/5adf01fc9791337ed17dd37236129ef3a0f4.pdf), sources: [[YingyuLiang/SemanticVector]](https://github.com/YingyuLiang/SemanticVector).
+- [2017 ACL] **FastText: Enriching Word Vectors with Subword Information**, [[paper]](https://arxiv.org/pdf/1607.04606.pdf), sources: [[facebookresearch/fastText]](https://github.com/facebookresearch/fastText), [[salestock/fastText.py]](https://github.com/salestock/fastText.py).
 - [2017 ICLR] **A Simple But Tough-to-beat Baseline for Sentence Embeddings**, [[paper]](https://openreview.net/pdf?id=SyK00v5xx), sources: [[PrincetonML/SIF]](https://github.com/PrincetonML/SIF).
 - [2017 NIPS] **Learned in Translation: Contextualized Word Vectors**, [[paper]](https://arxiv.org/pdf/1708.00107.pdf), sources: [[salesforce/cove]](https://github.com/salesforce/cove).
 - [2017 ArXiv] **Implicitly Incorporating Morphological Information into Word Embedding**, [[paper]](https://arxiv.org/pdf/1701.02481.pdf).