Natural Language Processing (NLP)
- How do I learn Natural Language Processing? | Sanket Gupta
- Natural Language Processing | Wikipedia
- Grammar Induction | Wikipedia
- Natural Language Tools
- 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)
- Latent Dirichlet Allocation (LDA)
- Autoencoders / Encoder-Decoders
- AI-Powered Search
- Neural Coreference
- Deep Q Learning (DQN)
- NLP News | Sebastian Ruder
- 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
- Language services | Cognitive Services | Microsoft Azure
- Text Transfer Learning
Speech recognition, speech translation, understanding complete sentences, understanding synonyms of matching words, sentiment analysis, and writing complete grammatically correct sentences and paragraphs.
Contents
- 1 Regular Expressions (Regex)
- 2 Tokenization
- 3 Stemming
- 4 Part-of-Speech (POS) Tagging
- 5 Chunking
- 6 Named Entity Recognition (NER)
- 7 Coreference
- 8 Semantic Role Labeling
- 9 Wikifier
- 10 Hierarchical Dataless Classifier
- 11 Lemmatization
- 12 Evaluation Matrics
- 13 Topic Modeling
- 14 Word Embeddings
- 15 Deep Learning Algorithms
- 16 NLP Pipeline
Regular Expressions (Regex)
Search for text patterns, validate emails and URLs, capture information, and use patterns to save development time.
Tokenization
Stemming
Part-of-Speech (POS) Tagging
Chunking
Named Entity Recognition (NER)
Coreference
Semantic Role Labeling
Wikifier
Hierarchical Dataless Classifier
Lemmatization
Evaluation Matrics
Confusion Matrix, Precision, Recall, F Score, ROC Curves, trade off between True Positive Rate and False Positive Rate.
Topic Modeling
Word Embeddings
Deep Learning Algorithms
NLP Pipeline
