Difference between revisions of "Natural Language Processing (NLP)"
| Line 6: | Line 6: | ||
* [[Doc2Vec]] | * [[Doc2Vec]] | ||
* [[Bag-of-Words (scikit-learn: Count Vectorizer)]] | * [[Bag-of-Words (scikit-learn: Count Vectorizer)]] | ||
| + | * [[LDA]] | ||
* [[SpaCy]] Python Library | * [[SpaCy]] Python Library | ||
* [[Autoencoders / Encoder-Decoders]] | * [[Autoencoders / Encoder-Decoders]] | ||
Revision as of 22:48, 6 September 2018
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Global Vectors for Word Representation (GloVe)
- Word2Vec
- Doc2Vec
- Bag-of-Words (scikit-learn: Count Vectorizer)
- LDA
- SpaCy Python Library
- Autoencoders / Encoder-Decoders
- AI-Powered Search
- Neural Coreference
- Deep Q Learning (DQN)
- 7 types of Artificial Neural Networks for Natural Language Processing
- Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts | Xingyou Wang, Weijie Jiang, Zhiyong Luo
