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The Vector-Space Similarity Model 7 also contains a Cosine Similarity Module as already mention. This is configured to discover out cosine similarity between https://moesport.com/?s=ทาง%20เข้า%20ufabet168%20ทาง%20เข้า the Average Document Vector 9 and each of the Prediction Vectors 8, both produced by the Random Indexing Term-Vector Map 7. The resulting similarity values are mapped to their respective predictions to provide a set of predictions with corresponding similarities eleven, that are passed to a Weighting Module 12. As acknowledged above, the modified chances for the text prediction components are utilized by the system to reorder the text prediction elements which have been generated by the system from consumer inputted text. The methodology based on claim 11, whereby the step of generating a vector map further comprises filtering out from the vector map terms with a very high incidence rate throughout the set of documents or stopwords. The summation is over the set of predictions and the intuitive interpretation of this formulation is that the similarity values do not provide unbiased evidence for the chance that a given time period or phrase should be predicted, quite that they are used to scale the present probabilities.
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In accordance with the current invention there's supplied a system and method which utilises a vector space technique, Random Indexing, to estimate the probability that a given time period or phrase belongs throughout the current textual context. The system subsequently generates a modified likelihood value for a given predicted term/phrase, based mostly on the probability that the term/phrase belongs inside the current context. The system makes use of the modified probabilities to reorder and/or choose the text predictions generated by a textual content prediction engine. The generated text predictions can then be displayed for user selection to input text into an electronic device.
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A document entered by the consumer can then be added to all or none of the Document Delimited Text Sources 4, or solely the most relevant Document Delimited Text Source 4. Again, to discover out essentially the most related Document Delimited Text Source 4, the system might use a classifier to classify the document to a specific topic. The system could then add this doc to a number of Document Delimited Text Sources, based on the outcomes of the classification. Although cosine similarity metrics are most popular, alternative vector similarity metrics such as Euclidean distance and dot product could be used.
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