nlp-tutorials

Tutorials For Beginners For Natural Language Processing

NLP Tasks

1. Text Classification
2. Text Similarity

• Sentence Encoder
• Cosine Similarity

3. Information Extraction

• Sentence Segmentation
• Word Tokenization
• Part of Speech Tagging
  • Named Entity Recognition
  • Key Phrase Extraction
• Syntactic Parsing
• Coreference Resolution
• Relation Extraction
• Event Extraction

4. Information Retrieval

• TFIDF score
• BERT
• Google Search
• Return the relevant website sorted by order

5. Chatbots
6. Machine Translator
7. Language Modeling
8. Text Summarization
9. Voice Assistants
10. Topic Modeling

NLP Pipeline

1. Data Acquisition
2. Text Extraction & Cleanup
3. Pre-processing

• Sentence Segmentation/tokenization
• Word Tokenization
• Stemming & Lemmatization

4. Model Building

• Feature Engineeering
• Machine Learning

5. Evaluation

• Confusion Matrix 2D,3D
• Accuracy, Precision, Recall, F1-score

6. Model Deployment
7. Monitor & Update

Text Presentation

• One Hot Encoding
• Bag of Words
• TF-IDF
• Word Embeddings

"Often in NLP, feeding a good text representation to an ordinary algorithm will get you much further compared to applying a top-notch algorithm to an ordinary text representation."

One hot encoding

• Doesn't capture the meaning of a word
• High memory consumption
• Out of vocabulary problem
• No fixed length representation

Bag of Words

• Large memeory consumption
• Doesn't capture the meaning of words properly