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Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words. If only one previous word is considered, it is called a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. [2]
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Anonymized e-mails and URLs. Omitted documents with lengths <500 words or >500,000 words, or that were <90% English. 7 billion Text 2011 [70] Shaoul, C., & Westbury C. NUS SMS Corpus SMS messages collected between two users, with timing analysis. ~ 10,000 XML NLP 2011 [71] KAN, M Reddit All Comments Corpus All Reddit comments (as of 2015). ~ 1. ...
This corpus first set the bar for the scientific study of the frequency and distribution of word categories in everyday language use. Compiled by Henry Kučera and W. Nelson Francis at Brown University , in Rhode Island , it is a general language corpus containing 500 samples of English, totaling roughly one million words, compiled from works ...
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.
In information retrieval, tf–idf (also TF*IDF, TFIDF, TF–IDF, or Tf–idf), short for term frequency–inverse document frequency, is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. [1]
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]