How do embeddings handle domain-specific vocabularies?

Embeddings handle domain-specific vocabularies by adjusting their vector representations based on the context and data they are trained on. When embeddings are created using general-purpose datasets (like Wikipedia or Common Crawl), they may struggle with specialized terms outside that scope. For example, a medical term like “tachycardia” might not be well-represented in a general language model. To address this, embeddings can be retrained or fine-tuned on domain-specific data, allowing them to capture the unique relationships and meanings of specialized terms within that field.

One approach is fine-tuning pre-trained embeddings using domain-specific corpora. For instance, a model like BERT can be further trained on medical journals to better understand terms like “myocardial infarction” or “hematopoiesis.” This process updates the model’s parameters to reflect how these terms are used in context, improving their vector representations. Similarly, in technical domains like software development, training embeddings on code repositories or API documentation helps terms like “dependency injection” or “idempotent” gain meaningful representations. Fine-tuning ensures that domain-specific terms are mapped to vectors that align with their usage in the target context, rather than relying on generic associations.

Another strategy involves building custom embeddings from scratch using domain-specific data. For example, a legal tech application might train embeddings solely on court cases and legal textbooks to capture precise meanings of terms like “habeas corpus” or “tortfeasor.” Tools like Word2Vec or FastText can be used here, as they allow developers to control the training data and parameters. Additionally, subword tokenization methods (e.g., Byte-Pair Encoding) help handle rare or compound terms by breaking them into smaller units. For instance, “neurodegenerative” might be split into “neuro,” “degen,” and “erative,” enabling the model to infer meaning even for unseen terms. By prioritizing domain data and tailored tokenization, embeddings can effectively represent specialized vocabularies.