Computational Linguistics and NLP

Computational Linguistics

Natural Language Processing (NLP)

• Subfield of artificial intelligence that deals with interaction between computers and human language
• Involves development of algorithms and statistical models to enable computers to process, understand, and generate natural language data
• Applications:
  • Sentiment analysis
  • Language translation
  • Text summarization
  • Question answering

Machine Learning

• Subset of artificial intelligence that enables computers to learn from data without being explicitly programmed
• Used in NLP to develop models that can learn from large datasets and improve over time
• Types of machine learning:
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
• Applications in NLP:
  • Language modeling
  • Sentiment analysis
  • Text classification

Language Modeling

• Subfield of NLP that focuses on developing statistical models to predict the next word in a sequence of text
• Goals:
  • Assign probabilities to sentences or sequences of words
  • Predict the next word in a sentence
  • Generate text that resembles human language
• Types of language models:
  • N-gram models
  • Neural network-based models
  • Recurrent neural network (RNN) models

Text Analysis

• Process of extracting useful information or insights from unstructured text data
• Techniques:
  • Tokenization
  • Part-of-speech tagging
  • Named entity recognition
  • Topic modeling
• Applications:
  • Sentiment analysis
  • Information retrieval
  • Text summarization

Speech Recognition

• Subfield of NLP that focuses on developing systems that can transcribe spoken language into text
• Challenges:
  • Variability in speech patterns
  • Noise in audio signals
  • Limited domain adaptation
• Techniques:
  • Acoustic modeling
  • Language modeling
  • Decoding algorithms
• Applications:
  • Voice assistants
  • Transcription systems
  • Speech-to-text systems

Natural Language Processing (NLP)

• Subfield of artificial intelligence that deals with interaction between computers and human language
• Involves development of algorithms and statistical models to enable computers to process, understand, and generate natural language data

Applications of NLP

• Sentiment analysis
• Language translation
• Text summarization
• Question answering

Machine Learning

• Subset of artificial intelligence that enables computers to learn from data without being explicitly programmed
• Used in NLP to develop models that can learn from large datasets and improve over time

Types of Machine Learning

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Applications of Machine Learning in NLP

• Language modeling
• Sentiment analysis
• Text classification

Language Modeling

• Subfield of NLP that focuses on developing statistical models to predict the next word in a sequence of text
• Goals:
  • Assign probabilities to sentences or sequences of words
  • Predict the next word in a sentence
  • Generate text that resembles human language

Types of Language Models

• N-gram models
• Neural network-based models
• Recurrent neural network (RNN) models

Text Analysis

• Process of extracting useful information or insights from unstructured text data
• Techniques:
  • Tokenization
  • Part-of-speech tagging
  • Named entity recognition
  • Topic modeling

Applications of Text Analysis

• Sentiment analysis
• Information retrieval
• Text summarization

Speech Recognition

• Subfield of NLP that focuses on developing systems that can transcribe spoken language into text
• Challenges:
  • Variability in speech patterns
  • Noise in audio signals
  • Limited domain adaptation

Techniques of Speech Recognition

• Acoustic modeling
• Language modeling
• Decoding algorithms

Applications of Speech Recognition

• Voice assistants
• Transcription systems
• Speech-to-text systems