Here’s a detailed explanation of each concept you mentioned about the **Word2Vec algorithm** — one of the foundational models in **Natural Language Processing (NLP)** for generating word embeddings:
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### 🔹 **Overview – Word2Vec Algorithm**
* **Word2Vec** is a **neural network–based algorithm** developed by **Tomas Mikolov and team at Google (2013)**.
* It transforms words from text into **numerical vector representations** (called *embeddings*) such that words with similar meanings have **similar vector representations**.
* These embeddings capture **semantic relationships** — meaning the geometry of the vectors reflects the meaning of the words.
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### 🔹 **Applications**
Word2Vec is a foundational technique used in many NLP tasks:
* **Sentiment Analysis:**
By representing words in vector form, models can identify patterns in sentiment-bearing words (e.g., “great” vs “terrible”).
* **Text Classification:**
Converts words into embeddings so that machine learning models can classify documents (e.g., spam vs non-spam).
* **Machine Translation:**
Helps align semantically similar words across languages.
* **Information Retrieval / Search Engines:**
Improves search accuracy by matching queries and documents with similar semantic meaning, not just exact words.
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### 🔹 **Core Idea – Mapping Words to Vectors**
* The main goal of Word2Vec is to **map each word** to a **dense, continuous-valued vector** in a high-dimensional space (typically 100–300 dimensions).
* These vectors capture **syntactic and semantic similarities** between words.
* Example:
* “king” – “man” + “woman” ≈ “queen”
* “walk” is close to “walking” and “ran” is close to “running”
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### 🔹 **Resulting Vector Representation – Word Embeddings**
* The **output of the Word2Vec model** is a set of **word embeddings**, i.e., numerical representations of words.
* Each embedding is a list of floating-point numbers that encode the word’s meaning based on **context** in which it appears.
* These embeddings can be reused across multiple NLP tasks (transferable knowledge).
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### 🔹 **Semantic Similarity – Words Close in Vector Space**
* In the embedding space:
* Words that appear in **similar contexts** have **similar vector representations**.
* The **cosine similarity** or **Euclidean distance** between vectors measures how related two words are.
* Example:
* `cosine_similarity(“cat”, “dog”)` → high (close meanings)
* `cosine_similarity(“cat”, “car”)` → low (different meanings)
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### 🔹 **Two Main Training Approaches**
Word2Vec can be trained using one of two neural network architectures:
1. **CBOW (Continuous Bag of Words):**
* Predicts a **target word** given its **context words**.
* Example: Given “the ___ barks”, predict “dog”.
* Faster for large datasets.
2. **Skip-Gram:**
* Predicts **context words** given a **target word**.
* Example: Given “dog”, predict likely surrounding words such as “barks”, “pet”, “animal”.
* Works better for smaller datasets and rare words.
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### 🔹 **Training Process (Simplified)**
1. Input text is tokenized into words.
2. The model creates a small neural network (usually one hidden layer).
3. During training, it learns to predict word–context pairs (for Skip-Gram) or context–word pairs (for CBOW).
4. The learned weights of the hidden layer become the **word embeddings**.
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### 🔹 **Key Characteristics**
* Produces **dense vectors** (compact representations).
* Captures **semantic meaning** and **linguistic relationships**.
* Trained on **large corpora** (e.g., Wikipedia, news data).
* Significantly more powerful than **one-hot encoding**, which captures no semantic relationship.
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### 🔹 **Advantages**
✅ Computationally efficient and scalable
✅ Captures semantic and syntactic word relationships
✅ Works well even with limited context size
✅ Can be fine-tuned for domain-specific corpora
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### 🔹 **Limitations**
⚠️ Cannot represent **out-of-vocabulary (OOV)** words (words unseen during training).
⚠️ Static embeddings — one vector per word, regardless of context (“bank” in “river bank” vs “money bank”).
⚠️ Doesn’t capture sentence-level meaning — only word-level semantics.
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