https://github.com/abdelrahman-amen/word-embedding

This code showcases text preprocessing (tokenization, stopword removal, and standardization), training a Word2Vec model to generate word embeddings, and analyzing word relationships using metrics like cosine similarity and Jaccard index. It also visualizes high-dimensional embeddings in 2D using MDS, illustrating how similar words cluster together
https://github.com/abdelrahman-amen/word-embedding

cosine-similarity euclidean-distances jaccard-similarity nlp nltk python sklearn tokenization wordembedding wordtovec

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This code showcases text preprocessing (tokenization, stopword removal, and standardization), training a Word2Vec model to generate word embeddings, and analyzing word relationships using metrics like cosine similarity and Jaccard index. It also visualizes high-dimensional embeddings in 2D using MDS, illustrating how similar words cluster together

• Host: GitHub
• URL: https://github.com/abdelrahman-amen/word-embedding
• Owner: Abdelrahman-Amen
• Created: 2025-01-14T16:08:52.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-01-14T16:46:56.000Z (9 months ago)
• Last Synced: 2025-01-14T18:12:08.031Z (9 months ago)
• Topics: cosine-similarity, euclidean-distances, jaccard-similarity, nlp, nltk, python, sklearn, tokenization, wordembedding, wordtovec
• Language: Jupyter Notebook
• Homepage:
• Size: 793 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🧠 Understanding Word Embeddings

Word embeddings are a powerful way to represent words in a continuous vector space. Unlike traditional one-hot encoding or bag-of-words approaches, word embeddings capture semantic relationships between words. For example, in a high-dimensional space, the vector for "king" might relate to "queen" in a way similar to how "man" relates to "woman."

![Word-Embedding](https://github.com/user-attachments/assets/0deb3cb1-3dcc-44d3-82b8-a52781cd7075)

# 🌟 What is Word2Vec?

Word2Vec is a technique developed by Tomas Mikolov and colleagues at Google in 2013. It is one of the most popular methods for generating word embeddings. Instead of representing words as discrete symbols, Word2Vec transforms them into dense, continuous vectors that capture syntactic and semantic properties.

# 🛠️ How Word2Vec Works

## 1. Architecture Options:

• Skip-gram Model: This model predicts the context words (nearby words) given a target word. It works well for smaller datasets and captures the semantics of rare words.

• CBOW (Continuous Bag of Words): This model predicts the target word based on its context words. It’s computationally efficient and works well for large datasets.

## 2. Training Mechanism:

• Word2Vec uses a neural network to learn the word embeddings. The network is trained to predict context words or target words depending on the model (Skip-gram or CBOW).

• During training, the model optimizes the word vectors to reduce the distance between words that appear in similar contexts.

## 3. Output:

• The result is a set of vectors where words with similar meanings are located close to one another in the vector space.

# 🔑 Key Advantages of Word2Vec

• Captures Semantics: Words like "dog" and "cat" are represented closely in the vector space due to their similar contexts.

• Analogies: Word2Vec vectors can solve analogies like "king - man + woman = queen."

![images](https://github.com/user-attachments/assets/5d7614e2-4f8a-404d-bb6e-8abdf7f01965)

• Efficiency: Word2Vec is computationally efficient and works well with large datasets.

# 🔍 Why Word Embeddings Matter

Word embeddings allow machine learning models to understand text in a more nuanced way. By representing words as vectors, these models can better capture relationships, synonyms, and contextual meanings, leading to improved performance in tasks like sentiment analysis, machine translation, and text summarization.

# Example

![1_sAJdxEsDjsPMioHyzlN3_A](https://github.com/user-attachments/assets/d82bb293-2713-4c9b-81b2-4cb1ba0ba3d7)

# 📚 Steps in the Code

## 1. 🔧 Importing Libraries and Dependencies

The notebook starts by importing essential libraries, such as:

• NLTK: For tokenization and stopword filtering.

• Gensim: For training and working with Word2Vec models.

• Scikit-learn: For similarity metrics like cosine similarity, Jaccard index, and Euclidean distances.

• NumPy: For numerical operations.

• Seaborn & Matplotlib: For data visualization.

These tools set the foundation for natural language processing and analysis tasks.

## 2. 📂 Downloading NLTK Resources

The code ensures that the necessary NLTK resources are available:

• punkt: A tokenizer that splits text into individual words or sentences.

• stopwords: A predefined list of common words (e.g., "the," "and") to filter out irrelevant terms.

This step prepares the data for preprocessing.

# 3. ✍️ Defining a Text Corpus

The corpus contains a rich paragraph explaining Natural Language Processing (NLP) and its applications. This text is used as the input data to:

• Tokenize words.

• Train Word2Vec embeddings.

• Perform similarity and visualization tasks.

# 4. 🧹 Preprocessing the Text

• Tokenization: The text is split into individual words using word_tokenize.

• Stopword Removal: Common, irrelevant words are filtered out to focus on meaningful terms.

• Lowercasing: Converts all words to lowercase to standardize the text and avoid redundancy.

These steps enhance the quality of input data for training word embeddings.

# 5. 🧠 Training Word2Vec

The Word2Vec model is trained on the preprocessed text using Gensim. Key parameters:

• vector_size: The dimensionality of the word vectors.

• window: The maximum distance between the current and predicted words.

• min_count: Ignores words that appear less than a specified number of times.

This results in a trained model where each word is represented by a dense vector in a semantic space.

# 6. 🔗 Word Similarity Analysis

• The notebook computes cosine similarity and other metrics to compare word vectors.

• Similar words are identified, and relationships between words in the vector space are explored.

# 7. 📊 Visualizing Word Embeddings

Using tools like MDS (Multidimensional Scaling) and distance matrices, the high-dimensional word embeddings are projected into a 2D space for visualization.

• Similar words are placed closer in the 2D plot.

• This gives a clear, interpretable view of the relationships between words.

# 8. 🎯 Distance Metrics

The code explores various distance and similarity measures:

• Cosine Similarity: Measures the angle between vectors.

• Jaccard Index: Compares the sets of common elements.

• Euclidean Distance: Measures the straight-line distance between two points.

Each metric is suited for different tasks, adding versatility to the analysis.