vektorutrymmesmodell

A vektorutrymmesmodell, often translated as vector space model, is a fundamental concept in information retrieval and natural language processing. It represents documents and queries as vectors in a high-dimensional space. Each dimension in this space corresponds to a unique term (word) from a predefined vocabulary. The value in each dimension for a given document vector indicates the importance or presence of that term within the document.

The process of creating these vectors typically involves tokenization, where text is broken down into individual words or terms. Stop words (common words like "the," "a," "is") are often removed, and stemming or lemmatization might be applied to reduce words to their root form. The most common method for assigning values to the dimensions is term frequency (TF), which measures how often a term appears in a document. However, to account for terms that are common across many documents but less informative, inverse document frequency (IDF) is often incorporated. This leads to the TF-IDF weighting scheme, where a term's weight is the product of its TF and IDF.

Once documents and queries are represented as vectors, their similarity can be calculated using mathematical measures, most commonly cosine similarity. This metric determines the angle between two vectors, with a smaller angle indicating higher similarity. This allows search engines to rank documents based on how relevant they are to a user's query. The vector space model provides a flexible and mathematically grounded approach to understanding and comparing textual data.