Embeddings handle rare words or objects through a few key strategies that help maintain their utility even when encountering terms that are not frequently represented in training datasets. One common approach is to use subword tokenization, which breaks down rare words into smaller, more manageable pieces or components. This allows the model to grasp the meanings of unfamiliar terms by leveraging the embeddings of the smaller parts. For example, the word “antidisestablishmentarianism” might be broken into subwords like “anti,” “dis,” and “establishment,” enabling the embedding to capture aspects of the word's meaning and context despite its overall rarity.
Another technique involves using a broader context to create associations between rare words or objects and their more common counterparts. When a rare word appears in a document, the surrounding context often includes other words or phrases that are more frequently used. Embedding models can take advantage of these contexts to learn and establish relationships between the rare word and its more common surrounding terms. Consequently, if the term “xylophone” appears near words like “musical” and “instrument,” the model can still generate embeddings that reflect its broader meaning in relation to music, even if it doesn't have a standalone common embedding.
Additionally, pre-trained embeddings can be fine-tuned on specific tasks or datasets that may contain these rare words. When a model is fine-tuned, it adjusts the existing vectors based on new data, allowing it to better capture the nuances of the rare words within that specific context. For instance, if a dataset on musical instruments includes references to various uncommon instruments, fine-tuning can produce refined embeddings that accurately represent those rare terms, helping to avoid the pitfalls of them being left out or poorly represented in the analysis. This flexibility ensures that rare words or objects are still effectively integrated into applications that rely on embeddings for tasks like text understanding or classification.