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2. Zamzar - Wikipedia

   en.wikipedia.org/wiki/Zamzar

   The service supports the conversion of documents, images, audio, video, e-Books, CAD files and compressed file formats. [ 6 ] [ 7 ] Users can type in a URL or upload one or more files (if they are all of the same format) from their computer; Zamzar will then convert the file(s) to another user-specified format, such as an Adobe PDF file to a ...

3. Semantic similarity - Wikipedia

   en.wikipedia.org/wiki/Semantic_similarity

   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 ...

4. Word2vec - Wikipedia

   en.wikipedia.org/wiki/Word2vec

   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 ...

5. Explicit semantic analysis - Wikipedia

   en.wikipedia.org/wiki/Explicit_semantic_analysis

   ESA was designed by Evgeniy Gabrilovich and Shaul Markovitch as a means of improving text categorization [2] and has been used by this pair of researchers to compute what they refer to as "semantic relatedness" by means of cosine similarity between the aforementioned vectors, collectively interpreted as a space of "concepts explicitly defined ...

6. Vector space model - Wikipedia

   en.wikipedia.org/wiki/Vector_space_model

   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 ...

7. Co-citation Proximity Analysis - Wikipedia

   en.wikipedia.org/wiki/Co-citation_Proximity_Analysis

   Figure visualizing the Co-citation Proximity Analysis (CPA) approach to document similarity computation. Co-citation Proximity Analysis (CPA) is a document similarity measure that uses citation analysis to assess semantic similarity between documents at both the global document level as well as at individual section-level.