Introduction:
NLP (Natural Language Processing) is a subfield of AI (Artificial Intelligence) and ML (Machine Learning) that focuses on enabling computers to understand and generate human language.
Why NLP?
- Google uses NLP for search engine recommendations.
- Artificial Intelligence (AI) aims to create applications that can perform tasks independently.
- Machine Learning (ML) provides statistical tools for data analysis and predictions.
- Deep Learning (DL) focuses on creating multi-layered neural networks to mimic human brain learning.
- NLP can be used in both ML and DL, as it deals with text data.NLP has high demand in research and industries.
Applications of NLP
NLP is used in various applications, including:
- Google News recommendations
- Google Translate
- Chatbots
- Information retrieval
- Spam classification
- Sentiment analysis
- Text summarization
Roadmap of NLP:
Text Preprocessing:
- Tokenization: Converting sentences into words.Stemming: Reducing words to their base form.
- Lemmatization: Converting words to their root form while preserving meaning.
Text Preprocessing Layer 2:
- Bag of Words: Representing text as a vector of word counts.
- TF-IDF: Weighting terms based on their frequency and inverse document frequency.
- N-grams: Combining sequences of words.
Advanced Text Preprocessing:
- Word Embeddings: Representing words as vectors that capture semantic similarities.
- Average Word2Vec: Calculating the average of word vectors to represent a document.
Deep Learning Techniques:
- Bi-directional LSTMs: Advanced neural networks for understanding context.
- Encoders and Decoders: Facilitating language translation.
- Attention Models: Focusing on specific parts of input sequences.
Advanced Deep Learning:
- Transformers: State-of-the-art models for NLP tasks.
- BERT: Bidirectional Encoder Representations from Transformers.
Tokenization
- Tokenization converts sentences into words.
- Example: "You are brilliant" → ["You", "are", "brilliant"]
Stop Words
- Stop words are common words like "the", "and", "of", etc.
- Stop words are common words that can be removed from text without changing its meaning.
- They can be removed to improve text processing efficiency.Stop words can be removed using a library like NLTK.
Stemming
- Stemming is a simpler process that removes suffixes and prefixes without considering the word's context.
- Example: "historical", "history", and "finalized" → "histor"
Lemmatization
- Lemmatization is a more sophisticated process that takes the word's context into account to determine its base form.
- Example: "historical", "history", and "finalized" → "history"
- Stemming can produce meaningless words, while lemmatization preserves the word's meaning.
Bag of Words
- Bag of Words (BOW) is a technique for converting text into vectors.
- BOW works by creating a vocabulary of unique words in the text and then counting the frequency of each word in the text.
- The resulting vector is a histogram of word frequencies.
- Example: ["You", "are", "brilliant"] → [1, 1, 1]
Libraries and Tools for NLP
- NLTK (Natural Language Toolkit): A popular Python library for NLP.
- spaCy: A Python library that provides high-performance NLP tools.
- TextBlob: A Python library for performing basic NLP tasks.
- TensorFlow: A widely used deep learning library that supports NLP applications.
