forespørselvektor

Forespørselvektor is a term used in information retrieval and natural language processing to represent a user's query as a vector in a high-dimensional space. This vectorization process allows for mathematical operations and comparisons between the query and documents, which are also represented as vectors.

The creation of a forespørselvektor typically involves several steps. First, the raw text of the user's query is preprocessed. This may include tokenization (breaking the text into individual words or terms), lowercasing, removing stop words (common words like "the," "a," "is"), and stemming or lemmatization (reducing words to their root form).

Once preprocessed, the terms in the query are assigned numerical weights. A common method for this is TF-IDF (Term Frequency-Inverse Document Frequency). TF-IDF assigns a higher weight to terms that appear frequently in the query (TF) but are rare across the entire collection of documents (IDF). This helps to emphasize the most important and distinguishing terms in the query.

The resulting weighted terms form the forespørselvektor. The dimensionality of this vector is usually equal to the size of the vocabulary of the document collection. Each dimension corresponds to a unique term, and the value in that dimension represents the weight of that term in the query.

By representing both queries and documents as forespørselvektorer, search engines can efficiently find relevant documents. Similarity measures, such as cosine similarity, are used to calculate the angle between the forespørselvektor and document vectors. A smaller angle indicates a higher degree of similarity, meaning the document is likely relevant to the user's query. This vector space model approach is fundamental to many modern search and recommendation systems.