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  2. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the ...

  3. 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 ...

  4. Medoid - Wikipedia

    en.wikipedia.org/wiki/Medoid

    Cosine similarity is a widely used measure to compare the similarity between two pieces of text. It calculates the cosine of the angle between two document vectors in a high-dimensional space. [14] Cosine similarity ranges between -1 and 1, where a value closer to 1 indicates higher similarity, and a value closer to -1 indicates lower similarity.

  5. Similarity measure - Wikipedia

    en.wikipedia.org/wiki/Similarity_measure

    Similarity measures are used to develop recommender systems. It observes a user's perception and liking of multiple items. On recommender systems, the method is using a distance calculation such as Euclidean Distance or Cosine Similarity to generate a similarity matrix with values representing the similarity of any pair of targets. Then, by ...

  6. Latent semantic analysis - Wikipedia

    en.wikipedia.org/wiki/Latent_semantic_analysis

    The similarity of terms or documents within these spaces is a factor of how close they are to each other in these spaces, typically computed as a function of the angle between the corresponding vectors. The same steps are used to locate the vectors representing the text of queries and new documents within the document space of an existing LSI ...

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Goldberg and Levy point out that the word2vec objective function causes words that occur in similar contexts to have similar embeddings (as measured by cosine similarity) and note that this is in line with J. R. Firth's distributional hypothesis. However, they note that this explanation is "very hand-wavy" and argue that a more formal ...

  8. HuffPost Data

    projects.huffingtonpost.com

    Visualization, analysis, interactive maps and real-time graphics. Browse, copy and fork our open-source software; Remix thousands of aggregated polling results;

  9. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization [citation needed].