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Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between items is based on the likeness of their meaning or semantic content [citation needed] as opposed to lexicographical similarity. These are mathematical tools used to estimate the strength of the semantic relationship between units of ...
Animation of the topic detection process in a document-word matrix. Every column corresponds to a document, every row to a word. A cell stores the weighting of a word in a document (e.g. by tf-idf), dark cells indicate high weights. LSA groups both documents that contain similar words, as well as words that occur in a similar set of documents.
Probabilistic latent semantic analysis (pLSA) [8] [9] and latent Dirichlet allocation (LDA) [10] are two popular topic models from text domains to tackle the similar multiple "theme" problem. Take LDA for an example. To model natural scene images using LDA, an analogy is made with document analysis: the image category is mapped to the document ...
The space of documents is then scanned using HDBSCAN, [20] and clusters of similar documents are found. Next, the centroid of documents identified in a cluster is considered to be that cluster's topic vector. Finally, top2vec searches the semantic space for word embeddings located near to the topic vector to ascertain the 'meaning' of the topic ...
Candidate documents from the corpus can be retrieved and ranked using a variety of methods. Relevance rankings of documents in a keyword search can be calculated, using the assumptions of document similarities theory, by comparing the deviation of angles between each document vector and the original query vector where the query is represented as a vector with same dimension as the vectors that ...
Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects ...
It is often applied in nearest neighbor search on large-scale high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. [3] A common approach for learning similarity is to model the similarity function as a bilinear form.
The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others.