Difference between revisions of "Natural Language Processing (NLP)"
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== Regular Expressions (Regex)== | == Regular Expressions (Regex)== | ||
| + | [http://www.youtube.com/results?search_query=Regex+Regular+Expression+nlp+natural+language Youtube search...] | ||
| + | |||
* [http://app.pluralsight.com/library/courses/code-school-breaking-the-ice-with-regular-expressions/table-of-contents Breaking the Ice with Regular Expressions | Code Schol] | * [http://app.pluralsight.com/library/courses/code-school-breaking-the-ice-with-regular-expressions/table-of-contents Breaking the Ice with Regular Expressions | Code Schol] | ||
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<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
| − | == Tokenization == | + | == Tokenization / Sentence Splitting == |
| + | [http://www.youtube.com/results?search_query=Tokenization+Sentence+Splitting+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Stemming == | == Stemming == | ||
| + | [http://www.youtube.com/results?search_query=Stemming+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Part-of-Speech (POS) Tagging == | == Part-of-Speech (POS) Tagging == | ||
| + | [http://www.youtube.com/results?search_query=POS+Part+Speech+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Chunking == | == Chunking == | ||
| + | [http://www.youtube.com/results?search_query=Chunking+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Named Entity Recognition (NER) == | == Named Entity Recognition (NER) == | ||
| + | [http://www.youtube.com/results?search_query=Named+Entity+Recognition+NER=nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Coreference == | == Coreference == | ||
| + | [http://www.youtube.com/results?search_query=Coreference+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Semantic Role Labeling == | == Semantic Role Labeling == | ||
| − | + | [http://www.youtube.com/results?search_query=Semantic+Role+Labeling+nlp+natural+language Youtube search...] | |
| − | |||
| − | |||
| − | |||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Hierarchical Dataless Classifier == | == Hierarchical Dataless Classifier == | ||
| + | [http://www.youtube.com/results?search_query=Hierarchical+Dataless+Classifier+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Lemmatization == | == Lemmatization == | ||
| + | [http://www.youtube.com/results?search_query=Lemmatization+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== [[Evaluation Matrics]] == | == [[Evaluation Matrics]] == | ||
| + | [http://www.youtube.com/results?search_query=Evaluation+Matrics+nlp+natural+language Youtube search...] | ||
| + | |||
Confusion Matrix, Precision, Recall, F Score, ROC Curves, trade off between True Positive Rate and False Positive Rate. | Confusion Matrix, Precision, Recall, F Score, ROC Curves, trade off between True Positive Rate and False Positive Rate. | ||
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== Topic Modeling == | == Topic Modeling == | ||
| + | [http://www.youtube.com/results?search_query=Topic+Modeling+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Word Embeddings == | == Word Embeddings == | ||
| + | [http://www.youtube.com/results?search_query=word+embeddings+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== Deep Learning Algorithms == | == Deep Learning Algorithms == | ||
| + | [http://www.youtube.com/results?search_query=deep+learning+nlp+natural+language Youtube search...] | ||
* [[LSTM]] | * [[LSTM]] | ||
* [[GRU]] | * [[GRU]] | ||
| + | |||
| + | <youtube>VrT3TRDDE4M</youtube> | ||
| + | |||
| + | == Wikifier == | ||
| + | [http://www.youtube.com/results?search_query=Wikifier+nlp+natural+language Youtube search...] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
== NLP Pipeline == | == NLP Pipeline == | ||
| + | [http://www.youtube.com/results?search_query=Pipeline+workflow+workbench+nlp+natural+language Youtube search...] | ||
* [https://github.com/CogComp/cogcomp-nlp/tree/master/pipeline CogComp NLP Pipeline | Cognitive Computation Group, led by Prof. Dan Roth] | * [https://github.com/CogComp/cogcomp-nlp/tree/master/pipeline CogComp NLP Pipeline | Cognitive Computation Group, led by Prof. Dan Roth] | ||
<youtube>VrT3TRDDE4M</youtube> | <youtube>VrT3TRDDE4M</youtube> | ||
Revision as of 09:45, 22 September 2018
- 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 / Sentence Splitting
- 3 Stemming
- 4 Part-of-Speech (POS) Tagging
- 5 Chunking
- 6 Named Entity Recognition (NER)
- 7 Coreference
- 8 Semantic Role Labeling
- 9 Hierarchical Dataless Classifier
- 10 Lemmatization
- 11 Evaluation Matrics
- 12 Topic Modeling
- 13 Word Embeddings
- 14 Deep Learning Algorithms
- 15 Wikifier
- 16 NLP Pipeline
Regular Expressions (Regex)
Search for text patterns, validate emails and URLs, capture information, and use patterns to save development time.
Tokenization / Sentence Splitting
Stemming
Part-of-Speech (POS) Tagging
Chunking
Named Entity Recognition (NER)
Coreference
Semantic Role Labeling
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
Wikifier
NLP Pipeline
