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A set of context vectors, one for every prediction 3, is retrieved from the Random Indexing Term-Vector Map 7 to generate Prediction Vectors eight. If the prediction is a phrase rather than a time period, the Prediction Vector eight associated with that prediction is generated as the arithmetic average of the context vectors for every time period inside the phrase. Some of the anticipated terms may not exist within the Random Indexing Term-Vector Map 7, because they could have been filtered out to filter out ‘noise’. In such instances, the chance value is not tailored by the Vector-Space Similarity Model 5.

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The technique additional comprises producing 25, utilizing a Cosine Similarity Module 10, similarity values 11 for the predictions by determining the cosine similarity between the Average Document Vector 9 and each of the Prediction Vectors 8. The methodology further comprises Modifying 26, utilizing a Weighting Module 12, the chances related to every text prediction utilizing the similarity

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values. Finally, the method contains reordering 27, using the Vector-Space Similarity Model 5, the text predictions three and outputting the reordered text predictions 6 for display to a person of an electronic gadget, and subsequent choice for entry into the electronic system. In accordance with the current invention there is supplied a system and technique which utilises a vector space approach, Random Indexing, to estimate the likelihood that a given time period or phrase belongs within the present textual context.

The predictions 3 could be word, phrase or punctuation predictions or the like which have been generated by the predictor 1. [newline]These predictions can be displayed to the user for user selection, to permit the consumer to progress or complete a sentence/document. Reordering the text predictions by the likelihood that they belong inside the current context provides the advantage of placing predictions which might be more more probably to be relevant to the current textual context on the high of an inventory for display and person selection, thereby facilitating user textual content enter. This is extremely advantageous where house for presentation to a user of textual content predictions is proscribed to a subset of the predictions.

If 1 or 2 occasions are thought-about void, no factors are awarded for eight correctly predicted outcomes. If three occasions are thought-about void, no factors are awarded for 8 and 9 correctly predicted outcomes. If an occasion is taken into account void, all of the outcomes of this event in all guess slips are considered successful.

The system is configured to determine the closeness in vector area between a vector representing a predicted time period and a vector representing the current text enter into an electronic device by a person. The system generates a modified probability value corresponding to each predicted time period based on the closeness of the 2 vectors in vector space. The current system subsequently generates an estimate of the chance that a predicted time period and a time period that has been inputted into a device by a consumer will happen throughout the same section of consumer inputted textual content.

A set of context vectors, one for every term in the present doc (i.e. the user inputted text) 2, is retrieved from the Random Indexing Term-Vector Map 7. The Average Document Vector 9 is generated by computing the arithmetic average of the context vectors for the phrases of the current document 2. As already mentioned, there is the chance that a few of the phrases from the current document won't be discovered in the Random Indexing Term-Vector Map 7, because these phrases have been filtered out for example.

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