Word embeddings play an important role in representing words in a format that machines can comprehend. Among the various word embedding techniques, GloVe (Global Vectors for Word Representation) stands out as a powerful and widely-used approach.
In this blog post, we'll delve into the concept of word embeddings and its application with GloVe in Python. The tutorial covers:
- The concept of word embedding
- Overview of GloVe
- Word embedding with Glove
- T-SNE visualization of Word2Vec
- Conclusion
Let's get started.
The concept of word embedding
Word embedding is a technique in NLP where words are represented as numerical vectors in a continuous space. In traditional NLP, words are often represented as discrete entities, devoid of any inherent relationship with one another. Word embeddings, however, transform words into continuous vector spaces, capturing semantic relationships and contextual meanings.
Overview of GloVe
GloVe, developed by the Stanford NLP Group, is an unsupervised learning algorithm designed to obtain vector representations for words. Unlike some approaches that rely solely on local context (such as Word2Vec) or global context (like Latent Semantic Analysis), GloVe achieves a balance by incorporating both local and global co-occurrence information.
Key features of GloVe:
- Global Context: Utilizes global statistics of the entire corpus to capture word relationships.
- Efficiency: Trains faster and more efficiently than some other methods.
- Captures Word Analogies: GloVe embeddings often perform well in tasks like word analogy completion.
How GloVe works
GloVe is based on the idea that the meaning of a word can be inferred from the co-occurrence probabilities with other words. The core concept involves constructing a word co-occurrence matrix, which is then factorized to obtain dense vector representations for each word.
Steps in GloVe embedding:
- Construct the Co-Occurrence Matrix: Count the number of times each word appears in the context of other words.
- Compute Word Probabilities: Normalize the co-occurrence counts to obtain probabilities.
- Define the Objective Function: Formulate an objective function that captures the relationship between word vectors.
- Optimization: Use optimization techniques (e.g., gradient descent) to minimize the objective function and obtain the optimal word vectors.
Word embedding with GloVe
Let's delve into the practical implementation of GloVe using Python and the 'gensim' library. Ensure 'gensim' is installed using the following command:
Additionally, it's necessary to download the GloVe vectors file, which is available here. Utilizing the 'gensim' library, we proceed to load the GloVe word-to-vector file into a GloVe model. Subsequently, we extract vectors for the word of interest and identify five similar words.
Similar words are displayed below:
T-SNE visualization of Word2Vec
GloVe embeddings represent words as high-dimensional vectors, with each dimension capturing a specific facet of the word's meaning. Although this information is rich, visualizing it in its raw form poses challenges. To address this, we employ T-SNE (T-distributed Stochastic Neighbor Embedding) visualization. T-SNE is a widely used technique for reducing high-dimensional data to two or three dimensions, enabling the visualization of relationships between words in our Word2Vec space. For this purpose, scikit-learn provides the TSNE class, designed for visualizing high-dimensional data.
In the following code snippet, we implement T-SNE visualization for words similar to 'ball'.
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