Handling misspellings and typos in semantic search requires a mix of preprocessing, model adjustments, and hybrid approaches. Semantic search focuses on understanding the intent behind a query rather than relying on exact keyword matches, but typos can still throw off results because misspelled words may not map well to the underlying data. To address this, developers typically use a combination of text normalization, spell-checking tools, and techniques that make embeddings more resilient to noise. The goal is to ensure the system interprets the user’s intent accurately, even when the input isn’t perfect.
First, preprocessing the query is a practical starting point. Tools like spellcheckers (e.g., SymSpell or Levenshtein distance algorithms) can correct obvious typos before the query reaches the semantic model. For example, if a user searches for “bablefish,” a spellchecker might correct it to “babel fish,” aligning it with stored content. Normalization steps like lowercasing, removing special characters, or expanding contractions (e.g., “don’t” to “do not”) also reduce variability. Additionally, fuzzy matching in databases like Elasticsearch can tolerate minor typos by allowing a small number of character mismatches. These steps help “clean” the input, increasing the chance of matching relevant content even if the query isn’t perfectly formatted.
Next, improving the semantic model’s robustness is key. Training or fine-tuning embedding models (e.g., SBERT or OpenAI’s embeddings) on noisy data—such as text with artificial typos—can help them recognize that “restrant” and “restaurant” are semantically similar. Another approach is to expand queries by generating synonyms or related terms (e.g., using WordNet or modern paraphrase models) to cast a wider semantic net. For instance, a search for “phne” could be expanded to include “phone,” “mobile,” or “device” to capture more relevant results. Some systems also use phonetic algorithms like Soundex, which encode words based on pronunciation, to handle errors where letters sound similar (e.g., “syntax” vs. “sintax”).
Finally, combining semantic and keyword-based methods often yields the best results. A hybrid system might use a traditional keyword search (like BM25) to retrieve a broad set of candidates, then rerank them using semantic similarity. This way, even if a typo slightly degrades the semantic score, the keyword match ensures the result isn’t missed. For example, a query for “cofee shops” might use BM25 to find documents containing “coffee” and then prioritize those most semantically aligned with “shops.” Tools like Elasticsearch’s “fuzzy” queries or AWS Kendra’s built-in typo tolerance demonstrate how platforms integrate these strategies by default. By layering these techniques, developers balance precision and recall, ensuring the system handles real-world queries effectively.