Handling rare or domain-specific vocabulary in NLP systems requires tailored techniques to ensure models understand and generate specialized terms accurately. Three effective approaches include subword tokenization, domain-specific pretraining, and integrating external knowledge sources. These methods help models process uncommon words without sacrificing performance on general language tasks.
Subword tokenization breaks words into smaller units, allowing models to handle rare or unseen terms. For example, Byte-Pair Encoding (BPE) splits words like "neurodegenerative" into "neuro," "degen," and "erative," enabling the model to recognize components shared with other terms. Similarly, WordPiece (used in BERT) and SentencePiece (common in multilingual models) create subword vocabularies optimized for specific datasets. This approach is particularly useful in domains like biomedicine, where complex compound words (e.g., "hemoglobinopathy") are frequent. By training tokenizers on domain-specific corpora, developers ensure rare terms are represented meaningfully rather than being mapped to generic "unknown" tokens.
Domain-specific pretraining adapts general-purpose language models to specialized fields. For instance, BioBERT starts with the base BERT architecture but continues training on medical literature, improving its grasp of terms like "angiogenesis" or "lymphoma." This technique works because models learn contextual relationships between rare terms and their common counterparts. A developer could fine-tune a legal document model by pretraining on case law texts, helping it distinguish between "tort" (civil wrong) and "torte" (a cake). The key is using a sufficiently large domain corpus—typically tens of thousands of documents—to update the model’s embeddings and attention patterns.
Integrating external knowledge bridges gaps when training data is scarce. Knowledge graphs like UMLS (Unified Medical Language System) map medical terms to standardized codes, letting models link "myocardial infarction" to synonyms like "heart attack." Custom dictionaries can enforce correct spellings for technical terms (e.g., "photosynthesis" vs. "fotosynthesis" in botany). Hybrid systems might use rule-based post-processing to replace model outputs with verified terms from a domain database. For example, a chemistry chatbot could cross-reference generated chemical names with PubChem entries to ensure accuracy. These methods combine the flexibility of neural models with structured domain expertise.