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ii. How to represent text data for machine learning models.
e. Machine Learning Basics
i. Supervised vs. unsupervised learning
ii. Model evaluation
iii. Common machine learning algorithms for NLP.
2.Basic of Mathematics for language Model
Linear algebra
Probability
N Gram Model
3. Machine Learning for NLP
Supervised Learning:
o Text Classification: Categorizing text based on predefined labels (e.g., sentiment analysis, spam detection).
o Information Extraction: Extracting structured information from text (e.g., named entity recognition, relationship extraction).
o Sentiment Analysis: Determining the emotional tone of text (positive, negative, neutral).
Unsupervised Learning:
o Topic Modeling: Discovering underlying themes or topics within a collection of documents.
o Clustering: Grouping similar documents or words together.
Common Machine Learning Algorithms
o Naive Bayes
o Support Vector Machines (SVMs)
o Decision Trees
o K-Nearest Neighbors (KNN)
Text Classification
o Supervised:
§ Bayesian Naive Bayes,
§ sentiment analysis
§ text classification
o Unsupervised:
§ Kmeans
§ Expectation-Maximization (EM) algorithm
§ MaxEnt classifier
Parts-of-Speech (POS) Tagging
o Hidden Markov Models
o SVMs
o CRF
o RNN
o LSTM
Parsing
o Linguistic Essentials
o Markov Models
o Applications of tagging
o Probabilistic parsing - CFG, CSG, PCFG
o Constituency Parsing
o Dependency Parsing
Language Modeling:
o N-gram Models
o Probabilistic Models
o Advanced Language Models
§ Hidden Markov Models (HMMs)
§ Conditional Random Fields (CRFs)
§ Recurrent Neural Networks (RNNs).·
4. Deep Learning for NLP
Introduction to Deep Learning
Recurrent Neural Networks (RNNs): Processing sequential data like text with models like LSTMs and GRUs.
Convolutional Neural Networks (CNNs): Extracting features from text using convolutional filters.
Transformers: A powerful architecture for NLP tasks, including language modeling and machine translation.
Pre-trained Language Models: Using models like BERT, RoBERTa, and GPT for various NLP applications.
5. Specific NLP Tasks
• Language Modeling: Predicting the probability of a sequence of words.
• Machine Translation: Translating text from one language to another.
• Question Answering: Answering questions based on a given text or knowledge base.
• Text Summarization: Generating concise summaries of longer texts.
• Dialogue Systems: Creating interactive chatbots and virtual assistants.
6. Advanced Topics
• Discourse Analysis: Understanding the relationships between sentences and paragraphs in a text.
• Pragmatics: Analyzing the contextual meaning of language and how it is used in different situations.
• Cross-lingual NLP: Working with multiple languages and languages that share characteristics.
7. Tools and Libraries
• Python: The dominant programming language for NLP.
• NLTK (Natural Language Toolkit): A widely used NLP library.
• SpaCy: Another popular NLP library known for its speed and efficiency.
• TensorFlow/PyTorch: Deep learning frameworks used for building and training NLP models.
• Scikit-learn: Python library for machine learning and statistical modeling
• Hugging Face Transformers: A library providing access to pre-trained language models.
8. Evaluation and Applications
• Metrics for NLP tasks:
o F1-score
o Precision
o Recall
o Accuracy
o AUC-ROC
o Other evaluation metrics.
• Feature Engineering for NLP:
o Bag-of-Words
o TF-IDF
o Word Embeddings (Word2Vec, GloVe, FastText)
• Applications of NLP: Chatbots, search engines, social media analysis, and more.
o Text Classification (e.g., Spam Detection, Sentiment Analysis)
o Named Entity Recognition (e.g., Extracting People, Locations, Organizations)
o Machine Translation (e.g., Translating English to Spanish)
o Question Answering (e.g., Answering Questions about a Text)
o Text Summarization (e.g., Generating Short Summaries of Articles)
9. Ethics and Societal Impact of NLP
1. Bias in NLP Models,
2. Responsible AI,
3. The Future of NLP
Course Outcome:
• Describe the basic ideas and models pertaining to social networks.
• Analyze the metrics, features, and structure of social networks.
• Utilize software tools to apply social network analysis measures to actual datasets.
• Use methods for social network security, community detection, and link prediction.
• Examine and simulate the information flow in social networks to optimize the cascade
Textbooks:
Hapke, Hannes, et al. Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python. United States, Manning, 2019.
Reference Books:
Pramod Singh, Machine Learning with PySpark: With Natural Language Processing and Recommender Systems, First Edition, Apress, 2018.
2. Rao, Delip, and McMahan, Brian. Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning. China, O'Reilly Media, 2019.
3. Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. United States, O'Reilly Media, 2019.
4. Eisenstein, Jacob. Introduction to Natural Language Processing. United States, MIT Press, 2019.
5. Vajjala, Sowmya, et al. Practical Natural Language Processing: A Comprehensive Guide to Building RealWorld NLP Systems. Taiwan, O'Reilly Media, 2020.
6. Raschka, Sebastian, and Mirjalili, Vahid. Python Publishing, 2017. Machine Learning. United Kingdom, Packt
7. Kochmar, Ekaterina. Getting Started with Natural Language Processing. United States, Manning, 2022. 8 Zhang, Yue, and Teng, Zhiyang. Natural Language Perspective. India, Cambridge University Press, 2021.
