Method, system and computer program product for learning classification model. The present invention provides a computer-implemented method for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units is provided. The method includes: obtaining a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units, the method comprising: obtaining a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data. 2. The method of claim 1, wherein the dedicated unit has a connection with each related output unit, each connection being initialized with a value stronger than other connections between the dedicated unit and remaining output units other than the related output units and between at least part of remaining hidden units among the plurality of the hidden units and the plurality of the output units including the related output units. 3. The method of claim 1, wherein the plurality of the hidden units forms one or more hidden layers and the plurality of the output units forms an output layer, the initializing the classification model comprising: initializing a weight matrix between the output layer and a hidden layer below the output layer, the weight matrix having a dedicated row for the combination of the co-occurring labels and remaining rows other than the dedicated row, the dedicated row having columns each corresponding to each co-occurring label, the columns being initialized with a value stronger than any remaining columns in the dedicated row other than columns corresponding to the co-occurring labels in the combination and other columns in the remaining rows. 4. The method of claim 2, wherein the value is an upper limit value for the connection or an upper bound value for normalized initialization. 5. The method of claim 2, wherein the value varies based on a frequency of the combination appeared in the one or more training data. 6. The method of claim 3, wherein the value varies based on a frequency of the combination appeared in the one or more training data. 7. The method of claim 2, wherein the initializing the classification model comprises: preparing connections between the dedicated unit and remaining output units with zero; and preparing at least part of remaining hidden units among the plurality of the hidden units with random initialization. 8. The method of claim 1, wherein the obtaining the combination of the co-occurring labels comprises: listing a plurality of combinations of labels co-occurred in the one or more training data; and selecting a subset from among the plurality of the combinations based on a frequency of appearance relevant to each combination in the one or more training data. 9. The method of claim 1, wherein the training input is a query in a form of a natural sentence or representation of the natural sentence and each correct label is a document identifier identifying a document having an answer for the query, the classification model performing a natural language query classification task. 10. The method of claim 1, wherein the training input is a text in a form of a natural sentence or representation of the natural sentence and each correct label is an attribute assigned for the text, the classification model performing a text classification task. 11. The method of claim 1, wherein the dedicated unit after the training still simultaneously activates the related output units corresponding to the co-occurring labels in the combination. 12. A computer-implemented method for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units, the method comprising: listing a combination of co-occurring labels appeared together in the one or more training data; initializing the classification model with preparing a dedicated unit for the combination, the dedicated unit having connections with related output units each corresponding to each co-occurring label in the combination, each connection being initialized with a value stronger than other connections with remaining output units and between at least part of remaining hidden units among the plurality of the hidden units and the plurality of the output units including the related output units; and training the classification model using the one or more training data. 13. A system for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units, the system comprising: a memory; a processor communicatively coupled to the memory; and a module for learning a classification model configured to carry out the steps of a method comprising: listing a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data. 14. The system of claim 13, wherein the dedicated unit of the method further comprises: a connection with each related output unit, each connection being initialized with a value stronger than other connections between the dedicated unit and remaining output units other than the related output units and between at least part of remaining hidden units among the plurality of the hidden units and the plurality of the output units including the related output units. 15. The system of claim 13, wherein the plurality of the hidden units forms one or more hidden layers and the plurality of the output units forms an output layer, the initializing the classification model step of the method further comprising the step of: initializing a weight matrix between the output layer and a hidden layer below the output layer, the weight matrix having a dedicated row for the combination of the co-occurring labels and remaining rows other than the dedicated row, the dedicated row having columns each corresponding to each co-occurring label, the columns being initialized with a value stronger than any remaining columns in the dedicated row other than columns corresponding to the co-occurring labels in the combination and other columns in the remaining rows. 16. The system of claim 14, wherein the value of the initializing a weight matrix step of the method is an upper limit value for the connection or an upper bound value for normalized initialization. 17. The system of claim 14, wherein the value of the initializing a weight matrix step of the method varies based on a frequency of the combination appeared in the one or more training data. 18. The system of claim 13, wherein the initializing a classification model step of the method further comprises the steps of: preparing connections between the dedicated unit and remaining output units with zero; and preparing at least part of remaining hidden units among the plurality of the hidden units with random initialization. 19. The system of claim 13, wherein listing a combination of co-occurring labels step of the method further comprises the steps of: listing a plurality of combinations of labels co-occurred in the one or more training data; and selecting a subset from among the plurality of the combinations based on a frequency of appearance relevant to each combination in the one or more training data. 20. The system of claim 13, wherein the system is provided in a cloud computing environment. 21. A system for learning a classification model by executing program instructions using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units, the system comprising: a memory; a processor communicatively coupled to the memory; and a module for learning a classification model configured to carry out the steps of a method comprising: obtaining a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data. 22. A non-transitory computer program product for learning a classification model using one or more training data each having a training input and one or more correct labels assigned to the training input, the classification model having a plurality of hidden units and a plurality of output units, including a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform the steps of a method comprising: obtaining a combination of co-occurring labels expected to be appeared together for an input to the classification model; initializing the classification model with preparing a dedicated unit for the combination from among the plurality of the hidden units so as to activate together related output units connected to the dedicated unit among the plurality of the output units, each related output unit corresponding to each co-occurring label in the combination; and training the classification model using the one or more training data. 23. The computer program product of claim 22, wherein the dedicated unit has a connection with each related output unit, each connection being initialized with a value stronger than other connections between the dedicated unit and remaining output units other than the related output units and between at least part of remaining hidden units among the plurality of the hidden units and the plurality of the output units including the related output units. 24. The computer program product of claim 23, wherein the value varies based on a frequency of the combination appeared in the one or more training data. 25. The computer program product of claim 22, wherein the obtaining the combination of the co-occurring labels comprises: listing a plurality of combinations of labels co-occurred in the one or more training data; and selecting a subset from among the plurality of the combinations based on a frequency of appearance relevant to each combination in the one or more training data.