14 Day Long Vary Forecast For Nassau




It may be anticipated that if two terms have occurred in exactly the identical set of paperwork within a set of training knowledge, they need to be ‘close’ within the vector area. Conversely, if phrases have occurred in disjoint units of paperwork then they want to be ‘distant’ in the vector space. The technique based on claim 19, additional comprising updating the vector map by assigning a new index vector to the finished textual content sequence and by adding the model new index vector to the sum of index vectors for every time period contained within the completed text sequence. The system in accordance with declare 2, wherein the processor is configured to replace the vector map by assigning a model new index vector to a completed textual content sequence enter and by adding the brand new index vector to the sum of index vectors for every term contained within the accomplished text sequence input. Once the e-mail has been accomplished by the consumer, this email is added to the Document Delimiting Text Source 4, which is used to train additional the predictor 1. Furthermore, the e-mail is assigned a model new index vector which is then added to the context vectors for all phrases contained in that doc to update the Indexing Term-Vector Map 7.

The worth given is a total predicted for the previous three hrs and includes the time of the forecast being checked out. The day label given represents the native day relative to the native time for the situation you are looking at. The text source used to train the predictor 1 of the system needn't be the Document Delimited Text Source 4. However, for optimal results, the Document Delimited Text Source four is used to train the predictor 1. The system of the invention includes additionally a Document Delimited Text Source 4, which is a set of textual knowledge organised into ‘documents’.

The technique according to claim eleven, wherein as quickly as a user has entered a complete text sequence, the strategy additional contains adding the finished textual content sequence to the set of documents. The technique based on claim 17, further comprising modifying the chances associated with every textual content prediction that has an equal within the vector map on the basis of the similarity values. The methodology in accordance with declare 11, wherein the vector map is a Random Indexing Term-Vector Map, and generating

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a vector map contains producing context vectors utilizing Random Indexing. The system in accordance with claim 4, whereby if the text prediction is a phrase, the processor is configured to generate a Prediction Vector comprising an average of the context vectors corresponding to each time period inside the phrase. The system based on claim three, whereby the processor is configured to generate a set of Prediction Vectors, comprising a context vector retrieved from the vector map for each text prediction that has an equal in the vector map. It might be appreciated that this description is by method of instance 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.

By method of non-limiting instance, for a vector space/distributional similarity model, one might use Latent Semantic Analysis, Probabilistic Semantic Analysis or Latent Dirichlet Allocation fashions. The current invention relates usually to a system and technique for inputting text into electronic devices. In specific the invention relates to a system and method for the adaptive reordering of textual content predictions for display and consumer selection. Text predictions are reordered to put predictions which might be extra more doubtless to be related to the present textual context at the top of an inventory for show and person selection, thereby facilitating user text input.

Furthermore, the entered term is used to generate the subsequent Average Document Vector 9 which is used to reorder a next set of predictions three and thus to generate a next reordered prediction set for consumer display and/or selection. To add the completed doc to the Random Indexing Term-Vector Map 7, it's assigned a brand new index vector which is then added to the context vectors for all phrases contained in that doc. In this fashion, the Random Indexing Term-Vector Map 7 is consistently up to date as new knowledge is acquired, and the system evolves over time/use. In the current invention, the system uses Random Indexing to map terms in a set of documents right into a vector space.

The predictions 3 may be word, phrase or punctuation predictions or the like which have been generated by the predictor 1. [newline]These predictions could be exhibited to the consumer for person selection, to allow the user to progress or complete a sentence/document. Reordering the textual content predictions by the chance that they belong inside the present context provides the benefit of placing predictions which are extra more doubtless to be relevant to the present textual context at the high of an inventory for display and user selection, thereby facilitating user text enter. This is very advantageous where house for presentation to a person of text predictions is proscribed to a subset of the predictions.

The technique additional comprises producing 25, using a Cosine Similarity Module 10, similarity values eleven for the predictions by determining the cosine similarity between the Average Document Vector 9 and every of the Prediction Vectors eight. The technique further comprises Modifying 26, using a Weighting Module 12, the probabilities associated with every textual content prediction using the similarity values. Finally, the strategy includes reordering 27, utilizing the Vector-Space Similarity Model 5, the textual content predictions 3 and outputting the reordered textual content predictions 6 for show to a user of an digital system, and subsequent selection for entry into the digital gadget. In accordance with the present invention there is provided a system and technique which utilises a vector house technique, Random Indexing, to estimate the chance that a given term or phrase belongs inside the current textual context.