The Vector Space Model (VSM) is a mathematical modeling technique often used in natural language processing and information retrieval. It represents text documents as vectors in a high-dimensional space. Each dimension corresponds to a specific term, and the value in each dimension reflects the importance of the term in the document.
In deep learning, VSM is particularly useful for tasks involving text data, such as sentiment analysis, document classification, and machine translation. It allows algorithms to understand and manipulate human language by converting text into numerical vectors that machines can process.
Word embeddings are one of the most common applications of VSM in deep learning. They represent words as dense vectors where a vector represents the projection of the word into a continuous vector space. The position of a word within the vector space is learned from text and is based on the words that surround the word when it is used. Word2Vec and GloVe are two popular models for creating word embeddings.
Using VSM in deep learning has several advantages:
Semantic Similarity: Words or documents close to each other in the vector space are semantically similar. This helps algorithms understand synonyms and context.
Dimensionality Reduction: Raw text data can have thousands or even millions of unique words. Representing this data as vectors reduces its dimensionality and makes it manageable for machine learning algorithms.
Noise Reduction: VSM can help filter out noise (irrelevant features) from text data by focusing on important terms.
Despite its advantages, using VSM in deep learning also has challenges like handling words with multiple meanings and capturing complex linguistic structures beyond simple word associations.
Word embedding models like Word2Vec are trained using two main architectures: Continuous Bag of Words (CBOW) and Skip-gram. Both methods use neural networks, but they differ in the way they predict context words.
Continuous Bag of Words (CBOW): This architecture predicts a target word (center word) from its surrounding context words. For instance, given the sentence "The cat sat on the mat," if we choose "sat" as the target word with a window size of 2, the input to the model would be ["The", "cat", "on", "the"], and it would try to predict "sat".
Skip-gram: This architecture does the opposite of CBOW; it predicts context words from a target word. Using the same example sentence and target word as before, now the model would take "sat" as input and try to predict ["The", "cat", "on", "the"].
Training these models involves feeding them massive amounts of text data. The neural networks then learn to assign weights to each input so that they can predict their targets accurately. These weights form the 'embeddings' or numerical vectors that represent each word.
During training, if a certain prediction is incorrect, an error is calculated using a loss function (like Negative Sampling or Hierarchical Softmax for Word2Vec). The model then adjusts its weights using this error.
Over time and with enough data, these models learn meaningful representations for words based on their contexts. Words used in similar contexts will have similar vector representations, allowing us to capture semantic relationships between words.
It's worth noting that these models do not understand the text in the way humans do; they merely learn patterns from how words appear together in the text data they are trained on.