To use embeddings for query expansion, you start by converting your search terms into vector representations that capture their semantic meaning. These embeddings allow you to find words or phrases that are contextually related to your original query, even if they don't share exact keywords. For example, if your initial query is "bike repair," a word embedding model might identify terms like "cycle maintenance," "tire replacement," or "chain adjustment" as semantically similar. By adding these related terms to the query, you increase the likelihood of matching relevant documents that use different phrasing. This approach works because embeddings map words into a numerical space where proximity indicates similarity in meaning, making it possible to expand queries beyond literal keyword matches.
To implement this, you can use pre-trained embedding models like Word2Vec, GloVe, or sentence transformers (e.g., Sentence-BERT). First, split the original query into individual terms or phrases and generate their embeddings. For multi-word queries, consider creating a single embedding for the entire phrase to preserve context. Next, compare these embeddings against a predefined vocabulary or dataset to find the closest matches using similarity metrics like cosine similarity. For instance, if your query is "cloud storage security," a sentence embedding model might suggest expanding it with terms like "data encryption," "server protection," or "AWS backup policies." Tools like FAISS or Annoy can speed up the similarity search process, especially when working with large datasets. You can then combine the top-N similar terms with the original query, either as synonyms or weighted additions, depending on your search engine’s capabilities.
However, there are practical considerations. First, ensure the embedding model aligns with your domain—a general-purpose model might miss niche terms. For example, a medical search system would benefit more from embeddings trained on clinical text than generic web data. Second, avoid overloading the query with irrelevant terms by setting a similarity score threshold (e.g., only include terms with a cosine similarity above 0.7). Third, test the impact of expansion on retrieval accuracy: adding too many terms could introduce noise. For instance, expanding "Python" with "snake" (from Word2Vec’s animal context) would be unhelpful in a programming-related search. Finally, consider hybrid approaches, like combining embeddings with traditional thesaurus-based expansion or user feedback, to balance precision and recall effectively.