What is Vectorization?
Text data is all around us, from books and articles to social media posts and customer reviews. However, for artificial intelligence (AI) systems to effectively process and understand this text data, it needs to be converted into a format that these systems can work with. This is where the process of vectorization, also known as embedding, comes into play.
Embedding, also known as vectorization, is a process of converting text data into numerical vectors or arrays of numbers. Each word, phrase, or document is represented as a unique vector, where similar texts have similar vector representations. This allows AI systems to work with text data in a way that they can understand and process.
AI models, particularly deep learning models, work with numerical data rather than raw text. However, this numerical data is not just a random collection of numbers; it is a carefully crafted numerical representation of the text data. Embeddings/vectorization allow AI models to understand and process text data by converting it into a meaningful numerical format.
For example, consider the words “king” and “queen.” While these words are clearly related in the context of royalty, their raw text representations (sequences of letters) don’t convey this similarity. However, through embeddings/vectorization, these words would be represented as numerical vectors that are close to each other in the vector space, reflecting their semantic similarity.
How Does Vectorization Work?
The basic idea behind vectors is that each word is mapped to a unique set of numbers based on its context and relationships with other words. These vectors can then be combined to represent larger pieces of text, such as sentences or documents.
One way to visualize this is to imagine a list of fruits (e.g., apple, banana, orange) and a list of junk food (e.g., candy bar, chips, soda). In the vector space created by vectorization process, the fruit vectors would be closer to each other, while the junk food vectors would be further away from the fruit vectors, reflecting their semantic differences. However, a “candy apple” may be somewhere in the middle.
Popular embedding/vectorization techniques include Word2Vec, GloVe, and BERT. Without going into technical details, these methods use neural networks and machine learning algorithms to learn the vector representations of words and texts from extremely large datasets of words, where semantic meaning and syntax can be more easily found.
Vectorization enable various natural language processing (NLP) tasks, such as text classification (categorizing texts into different topics or sentiments), machine translation (translating text from one language to another), and language generation (generating human-like text output).
In the real world, vectorization play a crucial role in applications like chatbots, content recommendation systems, and spam detection. For example, a chatbot powered by a retrieval-augmented generation (RAG) model might use vectors to efficiently search through a large corpus of documents to find the most relevant information to answer a user’s query, avoiding the “needle in a haystack” problem of poorly vectorized data.
Limitations
While vectorization has been instrumental in advancing NLP and AI, the process is not without limitations and challenges. One significant challenge is the need for large amounts of high-quality training data to learn accurate vector representations. Additionally, the computational complexity of these methods can be a hurdle, especially for resource-constrained environments.
Another limitation is that vectorization may not always capture certain nuances or context-specific meanings of language, leading to potential misunderstandings or errors in downstream applications.
Ongoing research aims to address these challenges by developing more efficient and contextually aware vectorization techniques, as well as exploring alternative approaches to representing and processing text data.