Introduction to NLP and Syntactic Processing

Begin your journey into Natural Language Processing (NLP) with an introduction to text data and encoding techniques, delving into the intricacies of regular expressions through extensive practice and use cases. Progress to lexical processing, learning to handle stopwords, split words, and implement bag-of-words and Tf-IDF models, applying these techniques to tasks like spam detection through detailed case studies. Advance to sophisticated lexical processing topics like spelling correction models and the Soundex algorithm, exploring practical implementations via Levenshtein Distance and spell correctors, and tackling challenges such as handling combined words like "New Delhi." This section solidifies your ability to preprocess and clean text data effectively. Transition to syntactic processing, covering parsing and grammar for English sentences, intermediate topics like stochastic parsing, the Viterbi algorithm, and Hidden Markov Models, reinforced through case studies and practical applications. Finally, tackle advanced syntactic processing techniques, including CFG grammar, top-down and bottom-up parsing, and probabilistic approaches like PCFG, concluding with a real-world project on information extraction through a comprehensive case study on ATIS flight reservations. Designed for aspiring NLP practitioners, data scientists, and software engineers, this course enhances understanding of syntactic processing, with a basic knowledge of Python programming and familiarity with machine learning concepts recommended.

• Introduction to NLP (Natural Language Processing) and Regex
• In this module, we will introduce the foundational concepts of Natural Language Processing (NLP) and delve into the mechanics of regular expressions (Regex). We will explore how to handle text data effectively and encode text for further processing. The module also includes a comprehensive look at Regex through multiple parts, culminating in practical use cases to solidify your understanding.
• Introduction to Lexical Processing
• In this module, we will explore the basics of lexical processing, starting with stopwords and word splitting techniques. We'll dive into the bag-of-words model and its application, followed by handling similar text words. The module concludes with case studies on applying these techniques in real-world scenarios, including spam detection and Tf-IDF analysis.
• Advanced Lexical Processing
• In this module, we will tackle more complex lexical processing tasks such as correcting spelling mistakes and using the Soundex algorithm for phonetic indexing. We will also work on case studies to implement these techniques practically. The module includes building spell correctors and handling combined words, providing a robust understanding of advanced lexical processing methods.
• Basic Syntactic Processing
• In this module, we will cover the basics of syntactic processing, starting with an understanding of what it entails. We'll look at parsing techniques and work on grammar rules for English sentences. The module includes case studies that apply lexicon-based and rule-based tagging techniques, helping you grasp the foundational aspects of syntactic analysis.
• Intermediate Syntactic Processing
• In this module, we will delve into intermediate syntactic processing, focusing on stochastic parsing and the Viterbi algorithm. We'll explore Hidden Markov Models and tackle decoding problems. The module includes case studies on Part-of-Speech (POS) tagging, HMMs, and the Viterbi algorithm, providing hands-on experience in intermediate syntactic processing.
• Advanced Syntactic Processing
• In this module, we will cover advanced syntactic processing techniques, including addressing issues with shallow parsing. We'll work with context-free grammar (CFG) and probabilistic CFG, and explore top-down and bottom-up parsing methods. The module includes detailed case studies, helping you understand practical issues and solutions in advanced syntactic processing.
• Probabilistic Approach
• In this module, we will focus on probabilistic approaches to syntactic processing. We will cover probabilistic context-free grammar (PCFG), and engage in case studies to apply these methods. The module also includes Chomsky Normal Form and dependency parsing techniques, providing a comprehensive understanding of probabilistic syntactic processing.
• Syntactic Processing with Real-World Project
• In this module, we will apply syntactic processing techniques to a real-world project, focusing on information extraction. We'll start with an introduction to the project and proceed with detailed case studies, specifically using ATIS flight reservations. This module is designed to consolidate your knowledge and skills by working through a comprehensive, practical application of syntactic processing.