One way vector space models consider context and semantics is through the use of term frequency-inverse document frequency (TF-IDF) weighting.
This technique assigns higher weights to terms that are more important or relevant to a particular document or query, based on how frequently they appear within the document or query compared to other documents in the collection.
For example, if the term "cancer" appears multiple times in a document about medical research, it would be given a higher weight than if it only appeared once in a document about fashion. This helps the model understand the context and meaning of the document or query and retrieve relevant results.
Another technique used in vector space models is stemming, which involves reducing words to their root form so that variations of the same word are treated as the same term. For example, "run," "ran," and "running" would all be stemmed to the root form "run." This helps the model understand the meaning of a query or document and retrieve relevant results, as it can see that these words are related to one another and have a common meaning.
Vector space models also often use stop words, which are common words that are typically ignored or removed from the text during the processing stage. These words, such as "the," "a," and "an," do not contribute much meaning to the context or semantics of the text and can often be removed without affecting the overall meaning. By removing these words, the model can focus on the more important terms and better understand the meaning of the text.
In addition, vector space models may use synonyms and related terms to help understand the context and semantics of a query or document. For example, if a user searches for "car," the model may also consider related terms such as "automobile" or "vehicle" to retrieve relevant results. This helps the model understand the broader context and meaning of the query, rather than just focusing on the specific term "car."
Finally, vector space models may also use techniques such as latent semantic indexing (LSI) and latent Dirichlet allocation (LDA) to extract and encode hidden relationships between words and concepts within the text. LSI, for example, uses singular value decomposition to identify patterns and relationships within the text and represent them in a lower-dimensional space, while LDA uses probabilistic models to identify hidden topics within the text and represent them as a set of weighted terms. These techniques help the model understand the underlying meaning and context of the text, rather than just the individual words.
Overall, vector space models take into account the context and semantics of search queries and documents through a variety of techniques, including weighting, stemming, stop words, synonyms, and latent semantic analysis. By considering these factors, the model is able to more accurately understand and represent the meaning and context of the text, leading to more relevant and accurate search results.