There is disclosed a computer implemented method of generating a training object for training a machine learning algorithm (MLA). The method comprises: acquiring a digital training document to be used in the training; transmitting the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method of generating a training object for training a machine learning algorithm, the training object including a digital training document and an assigned label, the method executable at a training server, the method comprising: acquiring the digital training document to be used in the training; transmitting, via a communication network, the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from the range of possible labels including at least a first possible label and a second possible label; obtaining from each of the plurality of assessors a selected label to form a pool of selected labels; generating a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and training the machine learning algorithm using the digital training document and the consensus label distribution. 2. The method of claim 1, the method further comprising: determining an expertise parameter for each of the plurality of assessors based on the pool of selected labels; and determining a difficulty parameter of the digital training document based on the pool of selected labels. 3. The method of claim 2, wherein: the expertise parameter is independent of the digital training document assessed; and the difficulty parameter is independent of any assessor assessing the digital training document. 4. The method of claim 3, wherein the consensus label distribution is determined by: determining an assessor-specific perceived label distribution for each assessor of the plurality of assessors; aggregating each of the assessor-specific perceived label distribution of the plurality of assessors. 5. The method of claim 4, wherein the assessor-specific perceived label distribution for a given assessor is determined by: for the first possible label: determining an assessor-inherent probability score of the first possible label being selected by the given assessor; determining a conditional probability score based at least on the expertise parameter and the difficulty parameter, the conditional probability score representing the probability of the selected label provided the given assessor perceived the first possible label as a most relevant label to the digital training document; and aggregating the assessor-inherent probability score and the conditional probability score to obtain a first label specific perceived score; for the second possible label: determining the assessor-inherent probability score of the second possible label being selected by the given assessor; determining the conditional probability score based at least on the expertise parameter and the difficulty parameter, the conditional probability score representing the probability of the selected label provided the given assessor perceived the second possible label as the most relevant label to the digital training document; aggregating the assessor-inherent probability score and the conditional probability score to obtain a second label specific perceived score; aggregating the first label specific perceived score and the second label specific perceived score. 6. The method of claim 5, wherein the assessor-inherent probability score is determined based at least on a given assessor's assessor-specific tendency parameter. 7. The method of claim 6, further comprising determining the given assessor's assessor-specific tendency parameter based at least from the given assessor's assessing history. 8. The method of claim 1, wherein the consensus label distribution is derived by maximizing likelihood of the selected label by using a formula: L  ( { y j w } ) = ∏ w ∈ Wj  ∑ z j w ∈  γ  Pr  ( z j w )  Pr  ( y j w  Z j = z j w ) . 9. The method of claim 8, wherein the formula is calculated using at least one modified model based on (i) a Dawid and Skene model, (ii) a GLAD model, and (iii) a minimax entropy principle. 10. The method of claim 1, wherein the machine learning algorithm is executed by a ranking application of a search ranker server, and wherein the training is based on a target of improving the accuracy of the machine learning algorithm. 11. The method of claim 10, wherein improving the accuracy represents improving a relevancy of a search result in response to a search request. 12. A training server for training a ranking application, the ranking application for ranking search results, the training server comprising: a network interface for communicatively coupling to a communication network; a processor coupled to the network interface, the processor configured to: acquire the digital training document to be used in the training; transmit, via the communication network, the digital training document to a plurality of assessors, transmitting further including indicating a range of possible labels for the assessors to assess from, the range of possible labels including at least a first possible label and a second possible label; obtain from each of the plurality of assessors a selected label to form a pool of selected labels; generate a consensus label distribution based on the pool of selected labels, the consensus label distribution representing a range of perceived labels for the digital training document and an associated probability score for each of the perceived labels; and train the machine learning algorithm using the digital training document and the consensus label distribution. 13. The training server of claim 12, the processor further configured to: determine an expertise parameter for each of the plurality of assessors based on the pool of selected labels; and determine a difficulty parameter or the digital training document based on the pool of selected labels. 14. The training server of claim 13, wherein: the expertise parameter is independent of the digital training document assessed; and the difficulty parameter is independent of any assessor assessing the digital training document. 15. The training server of claim 14, wherein the consensus label distribution is determined by the processor configured to: determine an assessor-specific perceived label distribution for each assessor of the plurality of assessors; aggregate each of the assessor-specific perceived label distribution of the plurality of assessors. 16. The training server of claim 15, wherein the assessor-specific perceived label distribution for a given assessor is determined by the processor configured to: for the first possible label: determining an assessor-inherent probability score of the first possible label being selected by the given assessor; determining a conditional probability score based at least on the expertise parameter and the difficulty parameter, the conditional probability score representing the probability of the selected label provided the given assessor perceived the first possible label as a most relevant label to the digital training document; and aggregating the assessor-inherent probability score and the conditional probability score to obtain a first label specific perceived score; for the second possible label: determining the assessor-inherent probability score of the second possible label being selected by the given assessor; determining the conditional probability score based at least on the expertise parameter and the difficulty parameter, the conditional probability score representing the probability of the selected label provided the given assessor perceived the second possible label as the most relevant label to the digital training document; aggregating the assessor-inherent probability score and the conditional probability score to obtain a second label specific perceived score; aggregating the first label specific perceived score and the second label specific perceived score. 17. The training server of claim 16, wherein the assessor-inherent probability score is determined based at least on a given assessor's assessor-specific tendency parameter. 18. The training server of claim 17, further comprising determining wherein the given assessor's assessor-specific tendency parameter based at least from the given assessor's assessing history. 19. The training server of claim 12, wherein the machine learning algorithm is executed by a ranking application of a search ranker server, and wherein the training is based on a target of improving the accuracy of the machine learning algorithm. 20. The training server of claim 19, wherein improving the accuracy represents improving a relevancy of a search result in response to a search request.