Computational Linguistics - Intermediate Course

Advancing Your Natural Language Processing Skills

What you'll learn

Apply natural language processing techniques to analyze and extract information from text data

Develop and evaluate machine learning models for text classification and sentiment analysis

Understand and implement common algorithms for syntactic parsing and machine translation

Design and implement a computational linguistics project, including data preprocessing, feature extraction, and model training and evaluation

Do NLP tasks with Generative AI

Requirements

Students should have a basic understanding of linguistic concepts, and basic programming skills, especially in Python. It is also recommended that students have completed the Computational Linguistics - Beginner Course.

Description

Are you ready to take your computational linguistics skills to the next level? This intermediate course dives deep into the foundational concepts of Natural Language Processing (NLP) while introducing advanced tools and techniques used in the field. Designed for students and professionals with basic knowledge of computational linguistics, this course blends solid theory with hands-on workshops to boost your expertise.  What You'll Learn:Introduction to NLP: A comprehensive overview of the key concepts underlying Natural Language Processing.  Hands-On Workshop with NLTK: Learn how to utilize this powerful Python library for linguistic analysis.  Exploring spaCy: Master this modern and efficient tool for large-scale NLP tasks.  Regular Expressions: Discover how to use regex for precise and efficient text processing.  Working with WordNet: Understand how to leverage this lexical database for semantic analysis and NLP tasks.  Generative AI and NLP: The most extensive section of the course, where you'll explore how to harness generative AI models for advanced tasks such as text generation, summarization, sentiment analysis, and more.  Why Enroll?  This course is designed to be practical and directly applicable. Each section includes interactive examples, guided exercises, and real-world projects to help you confidently tackle computational linguistics challenges.  Join today and become proficient in cutting-edge NLP tools and techniques with this comprehensive and up-to-date course!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 In the real world

Lecture 3 Strucuture of the course

Section 2: NLP Concepts

Lecture 4 Tokenization

Lecture 5 Lemmatization

Lecture 6 Stemming

Lecture 7 Part-of-Speech (POS) tagging

Lecture 8 Universal POS Tags

Lecture 9 Name Entity Recognition

Lecture 10 NER tags by Microsoft

Lecture 11 Stopwords Removal

Lecture 12 Sentiment Analysis

Section 3: Library: NLTK

Lecture 13 ipynb file NLTK

Lecture 14 What is NLTK?

Lecture 15 Getting the text to work with

Lecture 16 IDE Installation

Lecture 17 Tokenization with NLTK

Lecture 18 Lemmatization with NLTK

Lecture 19 Stemming with NLTK

Lecture 20 POS Tagging with NLTK

Lecture 21 Name Entity Recognition with NLTK

Lecture 22 Stopwords with NLTK

Lecture 23 Sentiment Analysis with NLTK

Section 4: Library: spaCy

Lecture 24 ipynb file spaCy

Lecture 25 What is spaCy?

Lecture 26 Tokenization with spaCy

Lecture 27 Lemmatization with spaCy

Lecture 28 POS Tagging with spaCy

Lecture 29 Name Entity Recognition with spaCy

Lecture 30 Sentiment Analysis with spaCy

Section 5: Library: Regular expressions

Lecture 31 Introduction

Lecture 32 Structure

Lecture 33 User of r" "

Lecture 34 Methods

Lecture 35 Methods for Match objects

Lecture 36 Exercise 1

Lecture 37 Solving Exercise 1

Lecture 38 Indentifiers

Lecture 39 Exercise 2

Lecture 40 Solving Exercise 2

Lecture 41 Metacharacters

Lecture 42 Exercise 3

Lecture 43 Solving Exercise 3

Lecture 44 Exercise 4

Lecture 45 Solving Exercise 4

Lecture 46 Quantifiers

Lecture 47 Exercise 5

Lecture 48 Solving Exercise 5

Lecture 49 Sets

Lecture 50 Modification

Lecture 51 Exercise 6

Lecture 52 Solving Exercise 6

Lecture 53 Exercise 7

Lecture 54 Solving Exercise 7

Lecture 55 Exercise 8

Lecture 56 Solving Exercise 8

Section 6: NLP tasks with Generative AI

Lecture 57 Getting started

Lecture 58 Getting our source text

Lecture 59 Getting our keys

Lecture 60 Checking official documentation

Lecture 61 Lemmatization with Generative AI

Lecture 62 POS Tagging with Generative AI

Lecture 63 Named Entity Recognition with Generative AI

Lecture 64 Sentiment Analysis with Generative AI

Lecture 65 Tailored responses based on sentiment with Generative AI

Section 7: Extra: Wordnet

Lecture 66 Introduction

Lecture 67 Main methods

Lecture 68 Applying methods to the code

Lecture 69 More moethods

Lecture 70 Exercise 1

Lecture 71 Exercise 1: Solved

Section 8: Conclusion

Lecture 72 Conclusion

This course is designed for linguists, translators, and other students with a background in linguistic-related studies who are interested in learning more about computational linguistics and natural language processing. If you have a basic understanding of Python, you will be able to follow along and apply the techniques covered in this course. If you are new to Python, don't worry! We recommend starting with the Computational Linguistics - Beginner Course to build a strong foundation before moving on to this intermediate-level course.