Natural Language Processing

I. INTRODUCTION

Welcome to the course on Natural Language Processing (NLP). In this course, we will explore the fascinating field of NLP, which involves developing algorithms and techniques that enable computers to process and understand human language. NLP has a wide range of applications, including text classification, sentiment analysis, named entity recognition, machine translation, and many others. As such, NLP is a rapidly growing field, and its impact is being felt in various industries, from finance and healthcare to education and entertainment. In this course, we will cover the key concepts and methods in NLP, including language modeling, part-of-speech tagging, named entity recognition, parsing and syntax, sentiment analysis, topic modeling, machine translation, and natural language generation. We will also discuss the different approaches to NLP, including rule-based methods, statistical methods, and neural methods, and explore the pros and cons of each approach. Throughout the course, we will use popular NLP libraries and frameworks, such as NLTK, spacey, and Tensor Flow, to develop practical skills in implementing NLP systems. We will also evaluate the performance of these systems and critically analyze their limitations and strengths.

II. COURSE OBJECTIVE:

By the end of the course, students should be able to:

• Understand the theoretical underpinnings of NLP, including language modeling, part-of-speech tagging, named entity recognition, parsing and syntax, sentiment analysis, topic modeling, machine translation, and natural language generation.
• Analyze and compare different NLP methods and techniques, including rule-based methods, statistical methods, and neural methods.
• Apply NLP techniques to real-world problems, such as text classification, sentiment analysis, named entity recognition, and machine translation.
• Develop practical skills in implementing NLP systems using popular libraries and frameworks, such as NLTK, spaCy, and Tensor Flow.
• Evaluate the performance of NLP systems and critically analyze their limitations and strengths.
• Identify and discuss ethical and societal issues related to NLP, such as bias, privacy concerns, and the impact of NLP on society.

III. COURSE OUTLINE:

Module 1: Introduction to NLP

• What is NLP and why is it important?
• The history of NLP and its current state-of-the-art
• The challenges and opportunities in NLP

Module 2: Text preprocessing

• Tokenization: breaking text into words or sub words
• Stop word removal: removing common words that don’t carry much meaning
• Stemming and lemmatization: reducing words to their base form

Module 3: Language modeling

• N-gram language models
• Neural language models (e.g., RNNs, LSTMs, Transformers)
• Evaluation metrics for language models

Module 4: Part-of-speech tagging

• What are parts of speech and why do we care?
• Hidden Markov Models (HMMs) for POS tagging
• Neural POS tagging with BiLSTMs or Transformers

Module 5: Named Entity Recognition (NER)

• What are named entities?
• Rule-based NER
• Neural NER with BiLSTMs or Transformers

Module 6: Sentiment analysis

• What is sentiment analysis and why is it useful?
• Supervised methods for sentiment analysis
• Unsupervised methods for sentiment analysis

Module 7: Machine translation

• What is machine translation?
• Rule-based machine translation
• Statistical machine translation
• Neural machine translation

Module 8: Advanced topics in NLP

• Text summarization
• Question answering
• Natural Language Generation
• Bias in NLP

IV. TARGET AUDIENCE:

By the end of the course, students should be able to:

• Understand the basic concepts and challenges in NLP, including language modeling, part-of-speech tagging, named entity recognition, parsing and syntax, sentiment analysis, topic modeling, machine translation, and natural language generation.
• Be familiar with the most common methods and techniques used in NLP, including rule-based methods, statistical methods, and neural methods.
• Develop practical skills in implementing NLP systems using popular libraries and frameworks, such as NLTK, spacey, and Tensor Flow.
• Critically evaluate the performance and limitations of NLP systems, and be aware of ethical and societal issues related to NLP, such as bias and privacy concerns.