Fundamentals of Natural Language Processing 3201-LST-FoNLP

This course provides an introduction to Natural Language Processing (NLP) for students familiar with
Python basics. Through theoretical and practical exercises, students will learn how to analyze and
process text data. The course covers essential NLP concepts and tools, including text preprocessing,
tokenization, sentiment analysis, and Named Entity Recognition (NER), as well as the use of
advanced libraries and solutions like NLTK, SpaCy, BERT and other transformers. By the end of the
course, students will be able to apply NLP techniques to solve real-world problems, such as sentiment
classification, entity recognition, or text summarization.
The course will cover the following topics:
● Introduction to NLP: Basic concepts, applications, and challenges
● Text Preprocessing: Tokenization, stop word removal, stemming, and lemmatization
● Data Structures for Text: Strings, lists, dictionaries, and handling text data in Pandas
● Basic Linguistic Concepts: Part-of-speech tagging and dependency trees
● Text Representation Techniques: Bag of Words, TF-IDF, word embeddings (Word2Vec,
GloVe)
● Text Classification: Building a simple classifier for sentiment or topic classification
● Named Entity Recognition (NER): Concepts, practical implementation using SpaCy, training
a custom NER model
● Introduction to Transformer Models: Overview of BERT and GPT, and their applications in
NLP
● Sentiment Analysis: Using libraries like TextBlob or pre-trained models
● Visualization of Text Data: Word clouds, frequency distributions, sentiment distributions, and
entity visualization