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The bets are cancelled in case if there may be some incorrectness within the names of the gamers, teams, there's a incorrect coefficient, a mistaken complete, a mistaken handicap, and so forth. If the bets are “Express””Chain” and “System”, the bets with mistaken information are calculated with 1 coefficient. There may be varied causes for altering a winning limit, depending on the provision and demand for a guess. Please notice that winning limits may also be lowered for a quick while when our bookmakers replace the percentages. It is usually potential to position the identical guess with higher stakes shortly thereafter.

An incorrectly said event time isn't thought to be a cause for cancellation of the bet. The method for producing a set of re-ordered output text predictions is now described in higher detail as regards to FIG. For the aim of a non-limiting example, will most likely be assumed that the area of the application is email, and that the Vector-space Similarity Model 5 has been trained on a set of email messages 4.

The system and methodology offers a way for reordering textual content predictions generated by the system, based on a probability that the predicted time period belongs in the section of text or text sequence which has been enter by a person. The reordering of text predictions locations the more than likely prediction candidates on the prime of a listing which is presented to the consumer for consumer selection. This facilitates text entry for the user by decreasing the labour involved in entering textual content, because it reduces/eliminates the requirement for the person to scroll via predicted terms to seek out the time period they intend to enter.

Random Indexing is a vector space approach used to generate context vectors representing terms within the vector area. Each context (e.g. every doc on this case) in a given part of data is assigned a unique and randomly-generated illustration known as an index vector. Random Indexing is an incremental technique, which implies that the context vectors can be used for similarity computations even after just some examples have been encountered. In the current system, each document 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 time period occurs.

For example, as mentioned beforehand, there are a selection of vector space/distributional similarity fashions than can be used to generate context vectors and map terms to a vector space. The system and technique of the present invention isn't therefore limited to the use of Random Indexing. The Random Indexing Term-Vector Map 7 can be used to generate an Average Document Vector 9.

A methodology based on the present invention is now described with reference to FIG. 3 which is a move chart of a method for processing consumer text input and producing a reordered set of textual content predictions, wherein the reordering of predictions relies on the likelihood that the anticipated time period or phrase belongs in a consumer inputted text sequence. In the actual technique described, step one includes receipt of person text input 20 in an electronic device.

A set of context vectors, one for every term in the current 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 common of the context vectors for the phrases of the current doc 2. As already talked about, there could be the possibility that a number of the phrases from the present document won't be discovered in the Random Indexing Term-Vector Map 7, because those phrases have been filtered out for example.