NLP-Tutorials

Introduction to NLP-Tutorials

Natural Language Processing (NLP) is a fascinating area of artificial intelligence that focuses on the interaction between computers and humans through natural language. The NLP-Tutorials repository offers a variety of simple yet powerful implementations of NLP models to help individuals better understand and practice the techniques used in this field.

Structure of the Repository

The repository is organized into several key areas that cover different aspects of NLP:

1. Search Engine:

   • TF-IDF: This section includes implementations of Term Frequency-Inverse Document Frequency (TF-IDF) using both NumPy and the Sklearn library. TF-IDF is a statistic that indicates the importance of a word in a document relative to a collection of documents (corpus).

2. Understanding Words (Word2Vec):

   • Continuous Bag of Words (CBOW) and Skip-Gram: These are two popular model architectures used in Word2Vec. Word2Vec is a technique to represent words in vector space, capturing semantics and syntactic similarities. The section provides code to implement both CBOW and Skip-Gram models.

3. Understanding Sentences (Seq2Seq):

   • Seq2Seq and CNN Language Model: Sequence to Sequence models are foundational in tasks like translation or summarization. The CNN language model provides an alternative approach by using convolutional neural networks for classifying sentences.

4. All About Attention:

   • Seq2Seq with Attention and Transformer: Attention mechanisms have revolutionized NLP by allowing models to focus on specific parts of the input sequence. This section includes implementations of Seq2Seq models enhanced with attention mechanisms and the Transformer model, which has become a standard in sequence transduction tasks.

5. Pretrained Models:

   • ELMo, GPT, and BERT: Pretrained models have significantly improved performance across various NLP tasks. This section covers three state-of-the-art models. ELMo (Embeddings from Language Models) provides context-sensitive embeddings, while GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) offer robust language understanding capabilities.

Credit and Contributions

The repository acknowledges the contributions of @W1Fl for simplified Keras code examples and @ruifanxu for a PyTorch version of the tutorial.

Installation

To get started with the NLP-Tutorials, clone the repository and install the necessary dependencies using pip:

$ git clone https://github.com/MorvanZhou/NLP-Tutorials
$ cd NLP-Tutorials/
$ sudo pip3 install -r requirements.txt

Detailed Model Implementations

TF-IDF

• NumPy Implementation: Offers a basic understanding of TF-IDF from scratch using NumPy.
• Sklearn Implementation: Illustrates a more efficient way to calculate TF-IDF using the Sklearn library tools.

Word2Vec

• Skip-Gram and CBOW Implementations: These examples provide insights into building word embeddings and understanding word relationships in a vector space.

Seq2Seq

• Basic Seq2Seq Model: Demonstrates how to set up a basic sequence to sequence model for tasks like translation.

CNN Language Model

• Leverages convolutional neural networks to classify sentences based on their content and context.

Seq2Seq with Attention

• Enhances the Seq2Seq model with attention mechanisms for improved performance on translation and similar tasks.

Transformer

• Provides an implementation of the Transformer model, which utilizes attention mechanisms to achieve exceptional performance on a variety of NLP tasks.

ELMo

• Implements ELMo embeddings, which capture deep contextual word representations.

GPT

• Features the Generative Pre-trained Transformer model, which improves language understanding through unsupervised learning.

BERT

• Discusses BERT and its strong bidirectional transformer architecture to grasp the context of words more deeply.

Each section not only gives a theoretical foundation with reference papers but also comes with accompanying code to offer practical insights into how these models are constructed and utilized.

The NLP-Tutorials repository is a rich resource for anyone keen to dive into the world of natural language processing, providing both theoretical knowledge and practical coding experience with a range of foundational and cutting-edge models. Users are encouraged to explore, practice, and even contribute to expanding the repository's offerings.