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The system based on declare 7, whereby the processor is configured to change the chance values associated with every textual content prediction that has an equivalent in the vector map on the basis of the similarity values. In this formulation, the proof from the similarity values and the prediction possibilities are considered independently, with weight assigned to every in accordance with the λ parameter. The λ parameter adjusts the burden between the contribution from the similarity values and from the likelihood values.
Furthermore, the entered time period is used to generate the next Average Document Vector 9 which is used to reorder a subsequent set of predictions 3 and thus to generate a next reordered prediction set for user display and/or selection. To add the finished doc to the Random Indexing Term-Vector Map 7, it's assigned a new index vector which is then added to the context vectors for all phrases contained in that document. In this way, the Random Indexing Term-Vector Map 7 is consistently updated as new data is acquired, and the system evolves over time/use. In the current invention, the system makes use of Random Indexing to map terms in a set of documents right into a vector space.
The context vector for every time period consists of the sum of index vectors for all documents in which that time period occurs. Therefore, when a new doc is completed, the sum of index vectors for every time period in that document comprises further the brand new index vector similar to that document. In this fashion, the Random Indexing Term-Vector Map 7 is continually up to date as new data is acquired. The Vector-Space Similarity Model 5 takes as inputs the current document 2, the Document Delimited Text Source four and the predictions three, and outputs a reordered set of predictions 6. The system in accordance with claim 1, additional comprising a vector map comprising a context vector for each time period of a plurality of terms in a set of paperwork, wherein the context vector contains a sum of index vectors for all documents of the set of documents in which a term happens.
The bets are cancelled in case if there's some incorrectness within the names of the gamers, teams, there is a mistaken coefficient, a wrong whole, a incorrect handicap, etc. If the bets are “Express””Chain” and “System”, the bets with mistaken information are calculated with 1 coefficient. There may be numerous causes for changing a profitable restrict, depending on the provision and demand for a wager. Please note that successful limits may be lowered for a short time when our bookmakers update the odds. It is usually attainable to position the identical wager with larger stakes shortly thereafter.
“Double” wager implies that one of the gamers of two teams should rating two or extra objectives in the course of the given match. In this kind of wager the primary image exhibits the end result of the primary half, and the second image reveals the end result of the game. For example, W1W2 guess means that the first group has won in the first half but the second staff has received within the total recreation.
The newly ordered list 6 can then be offered to the consumer for user selection. In the present technique example, say the user intended to enter the term ‘the’ and thus selects this time period for entry into the system. ‘the’ is handed to the predictor 1, along with the terms of the previous textual content sequence, to generate new textual content predictions 3.
For example, as mentioned beforehand, there are a number of vector space/distributional similarity fashions than can be utilized to generate context vectors and map terms to a vector space. The system and technique of the present invention is not therefore restricted to the use of Random Indexing. The Random Indexing Term-Vector Map 7 can be used to generate an Average Document Vector 9.
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