Writing a Comprehensive Self-Paced Roadmap for NLP with research papers
Writing a Comprehensive Self-Paced Roadmap for NLP with research papers, for my personal development.
Prerequisites:
Basic understanding of Python programming language
Familiarity with Machine and Deep Learning algorithms
Knowledge of libraries used in NLP such as Natural Language Toolkit (NLTK), spaCy, Core NLP, Te Blob, PyNLPI, Gensim, Pattern, etc
Text Preprocessing Level
Tokenization, Lemmatization, Stemming, Parts of Speech (POS), Stopwords removal, Punctuation removal
Understanding that textual data isn’t directly compatible with Machine Learning algorithms, so it needs to be preprocessed before feeding it into our Machine Learning models
Research Paper: “A Neural Probabilistic Language Model”
Advanced level Text Cleaning
Normalization, Correction of Typos
Mapping and Replacement. This involves mapping words to standardized language equivalents
Correction of Typos: Written text often contains errors
Research Paper: “A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning”
Language Modeling
Understanding of how to convert text data into numerical form
Types of Language Models: Statistical Language Models, Rule-based Language Models, and Neural Language Models
Research Paper: “The Unreasonable Effectiveness of Recurrent Neural Networks.”
Text Classification
Naive Bayes, Logistic Regression, Support Vector Machines (SVM), Random Forest
Understanding of how to classify text data.
Research Paper: “Convolutional Neural Networks for Sentence Classification”
Deep Learning for NLP
Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer Models
Understanding of how to use deep learning models for NLP
Research Paper: “Sequence to Sequence Learning with Neural Networks”
Advanced NLP Concepts
Named Entity Recognition (NER), Part-of-Speech tagging (POS), Sentiment Analysis
Understanding of advanced NLP concepts
Research Paper: “Named Entity Recognition with Bidirectional LSTM-CNNs”
Attention Mechanisms
Understanding of attention mechanisms
Types of Attention Mechanisms: Scaled-Dot Product Attention Mechanism, Multi-Head Attention Mechanism
Research Paper: “Attention is All You Need”
State-of-the-Art NLP Models
BERT, GPT-3, T5, ELECTRA
Understanding of state-of-the-art NLP models
Research Paper: “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”.
NLP Libraries and Frameworks
Hugging Face Transformers, AllenNLP
Understanding of various NLP libraries and frameworks
Research Paper: “Transformers: State-of-the-Art Natural Language Processing”.
NLP Projects
Text Classification, Sentiment Analysis, Chatbot Development.
Hands-on experience with NLP projects
Research Paper: “A Dataset for Document Grounded Conversations”.