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The methodology based on declare eleven, wherein as quickly as a person has entered a whole text sequence, the tactic additional contains adding the completed text sequence to the set of documents. The method according to claim 17, further comprising modifying the possibilities related to each textual content prediction that has an equal in the vector map on the basis of the similarity values. The technique based on claim 11, wherein the vector map is a Random Indexing Term-Vector Map, and generating a vector map comprises generating context vectors utilizing Random Indexing. The system according to claim 4, whereby if the textual content prediction is a phrase, the processor is configured to generate a Prediction Vector comprising a mean of the context vectors corresponding to each term inside the phrase. The system based on declare 3, whereby the processor is configured to generate a set of Prediction Vectors, comprising a context vector retrieved from the vector map for every textual content prediction that has an equivalent in the vector map. It might be appreciated that this description is by the use of example solely; alterations and modifications may be made to the described embodiment with out departing from the scope of the invention as defined in the claims.

Owing to the underlying Random Indexing mannequin being incremental, the present system and method allows for real-time updating as new ‘documents’ are created by the user. According to the present invention, when a consumer inputs text into an electronic system, the inputted text (i.e. current doc 2) is handed to no less than one predictor 1 and a Vector-Space Similarity Model 5. The person inputted text is break up into terms by a ‘tokeniser’ as is known within the art. The predictor 1 uses the tokenised user inputted textual content to generate time period or phrase predictions three. The text predictions three are handed to the Vector Space Similarity mannequin. The system and methodology based on the current invention therefore supplies an improved means of inputting textual content into digital gadgets.

The methodology further comprises producing 25, using a Cosine Similarity Module 10, similarity values eleven for the predictions by figuring out the cosine similarity between the Average Document Vector 9 and every of the Prediction Vectors 8. The technique further includes Modifying 26, utilizing a Weighting Module 12, the probabilities related to each text prediction utilizing the similarity values. Finally, the strategy includes reordering 27, utilizing the Vector-Space Similarity Model 5, the textual content predictions three and outputting the reordered textual content predictions 6 for display to a user of an digital system, and subsequent selection for entry into the electronic gadget. In accordance with the present invention there is supplied a system and technique which utilises a vector house method, Random Indexing, to estimate the likelihood that a given time period or phrase belongs inside the present textual context.

By means of non-limiting instance, for a vector space/distributional similarity mannequin, one might use Latent Semantic Analysis, Probabilistic Semantic Analysis or Latent Dirichlet Allocation fashions. The present invention relates generally to a system and technique for inputting textual content into electronic gadgets. In particular the invention relates to a system and methodology for the adaptive reordering of textual content predictions for display and user selection. Text predictions are reordered to position predictions that are extra more probably to be related to the current textual context at the high of a list for display and person selection, thereby facilitating person text enter.

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Random Indexing is a vector space approach used to generate context vectors representing terms within the vector space. Each context (e.g. each document on this case) in a given section of knowledge is assigned a singular and randomly-generated illustration referred to as an index vector. Random Indexing is an incremental technique, which implies that the context vectors can be used for similarity computations even after only a few examples have been encountered. In the current system, every doc is assigned a unique index vector and each term has a context vector associated with it. Each context vector consists of the sum of index vectors for all documents by which that term happens.

If the value of λ is ready at zero.5, this represents a choice to weight the two various kinds of worth equally. If the worth is set at zero, there will be no contribution from the similarities and consequently no change to the unique probabilities. Conversely, if the worth is about at 1, the final probabilities might be ruled totally on the premise of the similarities and the unique probabilities will be disregarded. This method is very suitable in situations the place you will want to be ready to explicitly control the contributions from the chance and similarity values. The summation is over the set of predictions and the intuitive interpretation of this formulation is that the similarity values do not provide impartial evidence for the likelihood that a given term or phrase must be predicted, rather that they are used to scale the prevailing possibilities. Consequently, if the similarity values are equal, the probabilities remain unchanged.

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