6. Word Embeddings

Word embedding is a fundamental technique in Natural Language Processing (NLP) that transforms words, which are discrete categorical data, into continuous vector spaces. This transformation is crucial because it allows machine learning algorithms to process and understand text data in a meaningful way, as these vectors capture and preserve syntactic and semantic relationships between words.

What are Word Embeddings?

At its core, word embedding represents each word as a dense, low-dimensional vector of real numbers. Unlike traditional methods like one-hot encoding, which create sparse and high-dimensional representations where words are independent, word embeddings learn relationships between words based on their usage in a corpus.

Example: Vector Representation

Consider the following example showing how words might be represented as vectors:

• blue: (0.01359, 0.00075, ..., -0.2524, 1.0048, 0.06259)
• blues: (0.01396, 0.11887, ..., -0.10007, 0.1158)
• orange: (-0.24776, -0.12359, ..., 0.23865, -0.014213)
• oranges: (-0.35609, 0.21854, ..., 0.38511, -0.070976)

Words with similar meanings or grammatical roles will be located closer to each other in this vector space. For instance, "blue" and "blues" would likely be close, as would "orange" and "oranges," reflecting their semantic and morphological relationships.

Word2Vec: Skip-Gram Model

Word2Vec is a popular family of models designed to learn word embeddings efficiently. Among its variants, the Skip-Gram model is particularly noteworthy. The Skip-Gram model works by predicting the context words surrounding a given target word.

Skip-Gram Model Explanation

The Skip-Gram model takes a target word and tries to predict words that are likely to appear in its vicinity (context). This is achieved by treating word prediction as a classification problem.

Skip-Gram Training Example

Consider the sentence: "The quick brown fox jumps over the lazy dog."

If we focus on the word "fox" and set a window size of 1, meaning we consider one word before and one word after the target word, the training pairs generated would be:

• Target Word: fox
• Context Words: brown, jumps

These pairs form the training data. For each pair, the input to the model is the target word's vector representation, and the desired output (label) is the context word's vector representation.

Implementation Steps with TensorFlow

Here's a breakdown of the implementation steps for training word embeddings using the Skip-Gram model with TensorFlow:

Step 1: Build Vocabulary

The first step is to create a vocabulary from the input text corpus. This involves tokenizing the text, converting it to lowercase, and then assigning a unique integer ID to each distinct word.

# Assume 'sentences' is a list of strings
word2index_map = {}
index = 0
for sent in sentences:
    for word in sent.lower().split():
        if word not in word2index_map:
            word2index_map[word] = index
            index += 1
index2word_map = {index: word for word, index in word2index_map.items()}
vocabulary_size = len(word2index_map)

This process results in a mapping from words to unique integer indices and vice versa, which is essential for numerical processing.

Step 2: Generate Skip-Gram Pairs

Once the vocabulary is built, we generate the training data in the form of (target_word_index, context_word_index) pairs. For each word in a sentence, we create pairs with its surrounding words within the defined window size.

# Assume 'sentences' and 'word2index_map' are defined
skip_gram_pairs = []
for sent in sentences:
    tokenized_sent = sent.lower().split()
    for i in range(len(tokenized_sent)):
        target_word = tokenized_sent[i]
        target_index = word2index_map[target_word]
        
        # Consider words within the window
        for j in range(max(0, i - window_size), min(len(tokenized_sent), i + window_size + 1)):
            if i != j:  # Don't pair a word with itself
                context_word = tokenized_sent[j]
                context_index = word2index_map[context_word]
                skip_gram_pairs.append([target_index, context_index])

This step prepares the dataset where each entry represents a target word and one of its contextual words.

Step 3: Define Embedding Layer

In TensorFlow, an embedding layer is typically represented as a variable matrix. Each row of this matrix corresponds to the embedding vector for a word, indexed by its integer ID. The tf.nn.embedding_lookup function is used to retrieve the embedding vectors for the input word indices.

import tensorflow as tf

# Assume vocabulary_size and embedding_dimension are defined
# Assume train_inputs is a tensor of word indices for training
embeddings = tf.Variable(
    tf.random_uniform([vocabulary_size, embedding_dimension], -1.0, 1.0),
    name='embedding'
)
embed = tf.nn.embedding_lookup(embeddings, train_inputs)

The embeddings variable is initialized with random values, and during training, these values are adjusted to learn meaningful word representations.

Step 4: Define NCE Loss

Training a full softmax classifier can be computationally expensive for large vocabularies. Noise-Contrastive Estimation (NCE) loss is a more efficient alternative. NCE loss approximates the softmax by treating the problem as distinguishing between the true context word and a small number of randomly sampled negative samples.

# Assume nce_weights, nce_biases, train_labels, negative_samples, vocabulary_size are defined
# Assume embed is the output from the embedding lookup (from Step 3)

loss = tf.reduce_mean(
    tf.nn.nce_loss(
        weights=nce_weights,
        biases=nce_biases,
        inputs=embed,
        labels=train_labels,
        num_sampled=negative_samples,
        num_classes=vocabulary_size
    )
)

NCE loss significantly speeds up training by only updating a subset of the model's parameters per step.

Step 5: Optimization

To train the model, an optimizer like Gradient Descent is used to minimize the defined loss function. A decaying learning rate is often employed to help the model converge more stably over time.

# Assume loss and global_step are defined
learningRate = tf.train.exponential_decay(0.1, global_step, 1000, 0.95, staircase=True)
train_step = tf.train.GradientDescentOptimizer(learningRate).minimize(loss)

The staircase=True argument makes the learning rate decrease in discrete steps, which can sometimes improve training dynamics.

Step 6: TensorBoard Embedding Visualization

TensorBoard's Embedding Projector is an invaluable tool for visualizing high-dimensional word embeddings. It allows you to interactively explore the embedding space, identify clusters of similar words, and understand the learned relationships.

import os
import tensorflow.compat.v1 as tf_v1 # For older TensorFlow versions
from tensorboard.plugins import projector

LOG_DIR = 'logs' # Directory to save logs

# ... (other setup code)

config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embeddings.name  # Name of the embedding variable
embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv') # Path to word metadata

# Save the configuration
with tf_v1.Session() as sess: # Use tf_v1.Session if using older TF versions
    # Initialize variables and potentially write metadata
    sess.run(tf_v1.global_variables_initializer())
    
    # Save embeddings and metadata for visualization
    saver = tf_v1.train.Saver()
    saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"), global_step=0)
    
    projector.visualize_embeddings(tf_v1.summary.FileWriter(LOG_DIR), config)
    
    # Ensure metadata.tsv is created if not already
    if not os.path.exists(embedding.metadata_path):
        with open(embedding.metadata_path, 'w') as f:
            for i in range(vocabulary_size):
                f.write(f"{index2word_map.get(i, '<UNK>')}\n")

To use this, you would run tensorboard --logdir=logs in your terminal and navigate to the "Embeddings" tab in your browser.

Step 7: Training Loop

The training loop iteratively feeds batches of data to the model, performs optimization, and periodically saves checkpoints.

# Assume get_skipgram_batch, merged (for summaries), sess, train_writer are defined
# Assume batch_size is defined

for step in range(1000):
    x_batch, y_batch = get_skipgram_batch(batch_size) # Function to fetch training batches
    
    summary, _ = sess.run([merged, train_step], feed_dict={train_inputs: x_batch, train_labels: y_batch})
    train_writer.add_summary(summary, step)
    
    # Periodically save the model
    if step % 100 == 0:
        saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"), global_step=step)

Saving checkpoints allows you to resume training or use the learned embeddings later.

Output and Applications

After training, the model produces:

• Dense Vector Representations: Each word in the corpus is represented by a dense, low-dimensional vector.
• TensorBoard Visualization: The Embedding Projector in TensorBoard provides a visual map of these vectors, highlighting clusters of semantically related words.
• Reusability: These learned embeddings can be reused as features in various downstream NLP tasks, such as text classification, sentiment analysis, machine translation, and question answering systems.

Summary

Word embeddings are a powerful technique for representing words in a continuous vector space, capturing their semantic and syntactic relationships. The Skip-Gram model, often trained with NCE loss for efficiency, is a foundational method for learning these embeddings. Tools like TensorFlow and TensorBoard facilitate the implementation and visualization of this process, making word embeddings a cornerstone of modern NLP.

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Interview Questions

• What is a word embedding and why is it used in NLP?
• Explain how the Skip-Gram model works in Word2Vec.
• What is the difference between Skip-Gram and CBOW (Continuous Bag-of-Words) models?
• How does Word2Vec capture semantic meaning in word vectors?
• What role does the tf.nn.embedding_lookup function play in TensorFlow?
• What is Noise Contrastive Estimation (NCE) loss, and why is it used in Word2Vec?
• How do you generate training pairs for the Skip-Gram model?
• How can you visualize trained word embeddings in TensorBoard?
• How does Word2Vec differ from newer models like BERT or GPT?
• Can you explain the importance of the context window size in Skip-Gram training?