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The Vector-Space Similarity Model 7 additionally comprises a Cosine Similarity Module as already point out. This is configured to determine cosine similarity between the Average Document Vector 9 and every 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 supply a set of predictions with corresponding similarities eleven, which are passed to a Weighting Module 12. As stated above, the modified possibilities for the text prediction elements are utilized by the system to reorder the textual content prediction elements which were generated by the system from person inputted textual content. The technique in accordance with declare eleven, whereby the step of producing a vector map additional comprises filtering out from the vector map terms with a really excessive incidence fee across the set of paperwork or stopwords. The summation is over the set of predictions and the intuitive interpretation of this formulation is that the similarity values don't provide unbiased proof for the probability that a given time period or phrase should be predicted, quite that they are used to scale the prevailing possibilities.
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In accordance with the present invention there's supplied a system and methodology which utilises a vector area technique, Random Indexing, to estimate the probability that a given term or phrase belongs within the present textual context. The system due to this fact generates a modified likelihood value for a given predicted term/phrase, based mostly on the chance that the term/phrase belongs inside the current context. The system uses the modified chances to reorder and/or select the textual content predictions generated by a textual content prediction engine. The generated text predictions can then be displayed for user selection to input text into an digital device.
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