Embedding for NLP with Deep Learning

Word Embedding was introduced by Bengio in early 2000s, and interest in it really accelerated when Google presented Word2Vec in 2013.

This blog post has recent papers related to embedding for Natural Language Processing with Deep Learning. Example application areas embedding is used for in the papers include finance (stock market prediction), biomedical text analysis, part-of-speech tagging, sentiment analysis, pharmacology (drug adverse effects).

I recommend you to start with the paper: In Defense of Word Embedding for Generic Text Representation

Best regards,

Amund Tveit

  1. An approach to the use of word embeddings in an opinion classification task
    – Authors: F Enríquez, Ja Troyano, T López (2016)
  2. Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews
    – Authors: B Li, T Liu, X Du, D Zhang, Z Zhao (2016)
  3. A Distributed Chinese Naive Bayes Classifier Based on Word Embedding
    – Authors: M Feng, G Wu (2016)
  4. An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
    – Authors: Y Lin, H Lei, J Wu, X Li (2015)
  5. Learning Bilingual Embedding Model for Cross-Language Sentiment Classification
    – Authors: X Tang, X Wan (2014)
  6. Training word embeddings for deep learning in biomedical text mining tasks
    – Authors: Z Jiang, L Li, D Huang, L Jin (2016)
  7. Learning Dense Convolutional Embeddings For Semantic Segmentation
    – Authors: Aw Harley, Kg Derpanis, I Kokkinos (2016)
  8. Creating Causal Embeddings for Question Answering with Minimal Supervision
    – Authors: R Sharp, M Surdeanu, P Jansen, P Clark, M Hammond (2016)
  9. Discriminative Phrase Embedding for Paraphrase Identification
    – Authors: W Yin, H Schütze (2016)
  10. Word embedding based retrieval model for similar cases recommendation
    – Authors: Y Zhao, J Wang, F Wang (2016)
  11. Learning Embeddings of API Tokens to Facilitate Deep Learning Based Program Processing
    – Authors: Y Lu, G Li, R Miao, Z Jin (2016)
  12. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation
    – Authors: C Wei, S Luo, X Ma, H Ren, J Zhang, L Pan (2016)
  13. Deep Learning Architecture for Part-of-Speech Tagging with Word and Suffix Embeddings
    – Authors: A Popov (2016)
  14. Robobarista: Learning to Manipulate Novel Objects via Deep Multimodal Embedding
    – Authors: J Sung, Sh Jin, I Lenz, A Saxena (2016)
  15. Pruning subsequence search with attention-based embedding
    – Authors: C Raffel, Dpw Ellis (2016)
  16. Sentence Embedding Evaluation Using Pyramid Annotation
    – Authors: T Baumel, R Cohen, M Elhadad (2016)
  17. Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval
    – Authors: H Palangi, L Deng, Y Shen, J Gao, X He, J Chen… (2015)
  18. Feedback recurrent neural network-based embedded vector and its application in topic model
    – Authors: L Li, S Gan, X Yin (2016)
  19. Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
    – Authors: P Li, H Huang (2016)
  20. Learning Word Meta-Embeddings by Using Ensembles of Embedding Sets
    – Authors: W Yin, H Schütze (2015)
  21. Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
    – Authors: J Cheng, D Kartsaklis (2015)
  22. A Deep Embedding Model for Co-occurrence Learning
    – Authors: Y Shen, R Jin, J Chen, X He, J Gao, L Deng (2015)
  23. Jointly Modeling Embedding and Translation to Bridge Video and Language
    – Authors: Y Pan, T Mei, T Yao, H Li, Y Rui (2015)
  24. Learning semantic word embeddings based on ordinal knowledge constraints
    – Authors: Q Liu, H Jiang, S Wei, Zh Ling, Y Hu (2015)
  25. Boosting Named Entity Recognition with Neural Character Embeddings
    – Authors: C Dos Santos, V Guimaraes, Rj Niterói, R De Janeiro (2015)
  26. Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese
    – Authors: Er Fonseca, Jlg Rosa, Sm Aluísio (2015)
  27. Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
    – Authors: J Liu, K Zhao, B Kusy, J Wen, R Jurdak (2015)
  28. Learning Feature Hierarchies: A Layer-wise Tag-embedded Approach
    – Authors: Z Yuan, C Xu, J Sang, S Yan, M Hossain (2015)
  29. Multi-Source Bayesian Embeddings for Learning Social Knowledge Graphs
    – Authors: Z Yang, J Tang (2015)
  30. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
    – Authors: J Tang, M Qu, Q Mei (2015)
  31. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
    – Authors: A Nikfarjam, A Sarker, K O’Connor, R Ginn, G Gonzalez (2015)
  32. Projective Label Propagation by Label Embedding
    – Authors: Z Zhang, W Jiang, F Li, L Zhang, M Zhao, L Jia (2015)
  33. An Investigation of Neural Embeddings for Coreference Resolution
    – Authors: V Godbole, W Liu, R Togneri (2015)
  34. Deep Multimodal Embedding: Manipulating Novel Objects with Point-clouds, Language and Trajectories
    – Authors: J Sung, I Lenz, A Saxena (2015)
  35. AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes
    – Authors: S Rothe, H Schütze (2015)
  36. Deep Multilingual Correlation for Improved Word Embeddings
    – Authors: A Lu, W Wang, M Bansal, K Gimpel, K Livescu (2015)
  37. Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks
    – Authors: Y Peng, H Jiang (2015)
  38. The Impact of Structured Event Embeddings on Scalable Stock Forecasting Models
    – Authors: Jb Nascimento, M Cristo (2015)
  39. Representing Text for Joint Embedding of Text and Knowledge Bases
    – Authors: K Toutanova, D Chen, P Pantel, H Poon, P Choudhury… (2015)
  40. In Defense of Word Embedding for Generic Text Representation
    – Authors: G Lev, B Klein, L Wolf (2015)
  41. Learning Multi-Relational Semantics Using Neural-Embedding Models
    – Authors: B Yang, W Yih, X He, J Gao, L Deng (2014)
  42. Improving relation descriptor extraction with word embeddings and cluster features
    – Authors: T Liu, M Li (2014)

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