Search results
Results from the WOW.Com Content Network
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.
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 ...
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 ...
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 ...
In a 2005 paper by Fergus et al., [1] pLSA (probabilistic latent semantic analysis) and extensions of this model were applied to the problem of object categorization from image search. pLSA was originally developed for document classification, but has since been applied to computer vision.
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.