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Word Embeddings

Table of Contents

  1. Introduction
  2. Why Use Word Embeddings?
  3. Types of Word Embeddings
  4. Word2Vec
  5. GloVe (Global Vectors for Word Representation)
  6. FastText
  7. ELMo (Embeddings from Language Models)
  8. BERT (Bidirectional Encoder Representations from Transformers)
  9. Applications of Word Embeddings
  10. Example Code
  11. Conclusion
  12. References

Introduction

Word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. These embeddings capture semantic meanings of words based on their context and usage in a given corpus. Word embeddings have become a fundamental concept in Natural Language Processing (NLP) and are widely used for various NLP tasks such as sentiment analysis, machine translation, and more.

Why Use Word Embeddings?

Traditional word representations, such as one-hot encoding, have limitations: - High Dimensionality: One-hot encoding results in very high-dimensional vectors, which are sparse (mostly zeros). - Lack of Semantics: One-hot vectors do not capture any semantic relationships between words.

Word embeddings address these issues by: - Dimensionality Reduction: Representing words in a lower-dimensional space. - Capturing Semantics: Encoding words such that similar words are closer together in the vector space.

Types of Word Embeddings

1. Word2Vec

Developed by Mikolov et al., Word2Vec generates word embeddings using neural networks. There are two main models:

  • Continuous Bag of Words (CBOW): Predicts a target word based on its surrounding context words.
  • Skip-gram: Predicts surrounding context words given a target word.

Advantages

  • Efficient and scalable.
  • Captures semantic similarity.

Limitations

  • Context window size and other parameters need tuning.
  • Does not consider word order beyond the fixed context window.

2. GloVe (Global Vectors for Word Representation)

Developed by Pennington et al., GloVe generates embeddings by factorizing the word co-occurrence matrix. The key idea is to use the global statistical information of the corpus.

Advantages

  • Captures global statistical information.
  • Produces high-quality embeddings.

Limitations

  • Computationally intensive.
  • Fixed window size and parameters.

3. FastText

Developed by Facebook's AI Research (FAIR) lab, FastText is an extension of Word2Vec that represents words as bags of character n-grams. This allows FastText to generate embeddings for out-of-vocabulary words.

Advantages

  • Handles morphologically rich languages better.
  • Generates embeddings for out-of-vocabulary words.

Limitations

  • Slightly more complex to train than Word2Vec.
  • Increased training time.

4. ELMo (Embeddings from Language Models)

Developed by Peters et al., ELMo generates embeddings using deep contextualized word representations based on a bidirectional LSTM.

Advantages

  • Contextual embeddings.
  • Captures polysemy (different meanings of a word).

Limitations

  • Computationally expensive.
  • Not as interpretable as static embeddings.

5. BERT (Bidirectional Encoder Representations from Transformers)

Developed by Devlin et al., BERT uses transformers to generate embeddings. BERT's embeddings are contextual and capture bidirectional context.

Advantages

  • State-of-the-art performance in many NLP tasks.
  • Contextual embeddings capture richer semantics.

Limitations

  • Requires significant computational resources.
  • Complexity in implementation.

Applications of Word Embeddings

  • Sentiment Analysis: Understanding the sentiment of a text by analyzing word embeddings.
  • Machine Translation: Translating text from one language to another using embeddings.
  • Text Classification: Categorizing text into predefined categories.
  • Named Entity Recognition: Identifying and classifying entities in text.

Example Code

Here's an example of using Word2Vec with the gensim library in Python:

from gensim.models import Word2Vec
from nltk.tokenize import word_tokenize
import nltk

nltk.download('punkt')

# Sample corpus
corpus = [
    "This is a sample sentence",
    "Word embeddings are useful in NLP",
    "Natural Language Processing with embeddings"
]

# Tokenize the corpus
tokenized_corpus = [word_tokenize(sentence.lower()) for sentence in corpus]

# Train Word2Vec model
model = Word2Vec(sentences=tokenized_corpus, vector_size=50, window=3, min_count=1, sg=1)

# Get the embedding for the word 'word'
word_embedding = model.wv['word']
print("Embedding for 'word':", word_embedding)

Conclusion

Word embeddings are a powerful technique in NLP that provide a way to represent words in a dense, continuous vector space. By capturing semantic relationships and reducing dimensionality, embeddings improve the performance of various NLP tasks and models.

References