A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for disambiguating one or more entity mentions in one or more documents, comprising: receiving, at at least one processor, a set of one or more entity mentions in a document and context data associated with each entity mention; receiving, at the at least one processor, a set of one or more target candidate entities that potentially refers to or describes the entity mentions in the document; running, by the at least one processor, convolutional neural network (CNN) models for identifying and learning local representations associated with each entity mention and target candidate entity and associated context; running recurrent neural networks (RNN) model operations on the at least one processor over the representations of the entity mentions and target candidate entities of the document to capture a topical coherence between the entity mentions and the target candidate entities; and providing a link for each entity mention to link to a respective the target candidates entity in the document based on the identified local features and the topical coherence from the convolutional neural networks and recurrent neural networks. 2. The computer-implemented method of claim 1, further comprising: encoding multiple variables, each variable comprising a word sequence including an entity mention and associated context, or target candidates, or both the word sequence including an entity mention and associated context and target candidates; transforming each encoded variable into a corresponding encoded vector using a word embedding table; and said at least one processor running CNN model operations on said encoded vectors to obtain a distributed representation for the encoded vectors. 3. The computer-implemented method of claim 2, further comprising: utilizing a set of multiple window sizes to parameterize the CNN model operations on said encoded vectors, each window size corresponds to a convolution matrix of a predetermined dimensionality. 4. The computer-implemented method of claim 3, further comprising: concatenating the resulting vectors for each window size for representation as the distributed representation for encoded variable as a single concatenation vector. 5. The computer-implemented method of claim 3, further comprising: computing, at the at least one processor, a relevance score for the target candidates said using CNN model operations; ranking a forward linking, based on the target candidate's relevance score; and providing, by the at least one processor, forward linking of the entity mentions to identified target candidates. 6. The computer-implemented method of claim 5, further comprising: using RNNs model operations run at the at least one processor, to further provide backward linking of the entity mentions to identified target candidates and ranking said backward linking, based on the computed relevance score; and combining, at the at least one processor, forward linking ranks and backward linking ranks, to create a combined linking rank when training and applying the model operations. 7. The computer-implemented method of claim 5, further comprising: computing, using the local features from the CNN model operations, similarities between each entity mention and pages of said target candidate entities, said computing of local features learned using said entity mentions and pages comprising: computing a first weight and a second weight for a respective feature vector and feature vector, where is a sparse feature vector capturing one or more linguistic properties and linking statistics, and where is generated using the cosine similarities between the encoded vectors at multiple granularities of entity mentions and pages of said target candidate entity. 8. The computer-implemented method of claim 5, further comprising: receiving, at said at least one processor running said RNN model operations for a current entity mention, said distributed representations for the encoded vectors; and using said RNN to accumulate information about a previous entity mention and target candidate entities, and providing them as the global constraints for the linking process of the current entity mention. 9. The computer-implemented method of claim 5, further comprising: computing of the ranking scores for all the target candidates of all the entity mentions in each document simultaneously, while preserving the order of the entity mentions from the beginning to the end of an input document. 10. A computer system for disambiguating one or more entity mentions in one or more documents, comprising: at least one processor; a memory storing instructions to be run at said at least one processor; said instructions configuring said at least one processor to perform a method to receive a set of one or more entity mentions in a document and context data associated with each entity mention; receive a set of one or more target candidate entities that potentially refers to or describes the entity mentions in the document; run convolutional neural network (CNN) models for identifying and learning local representations associated with each entity mention and target candidate entity and associated context; run recurrent neural networks (RNN) model operations over the representations of the entity mentions and target candidate entities of the document to capture a topical coherence between the entity mentions and the target candidate entities; and provide a link for each entity mention to link to a respective the target candidates entity in the document based on the identified local features and the topical coherence from the convolutional neural networks and recurrent neural networks. 11. The computer system of claim 10, wherein said at least one processor is further configured to: encode multiple variables, each variable comprising a word sequence including an entity mention and associated context, or target candidates, or both the word sequence including an entity mention and associated context and target candidates; transform each encoded variable into a corresponding encoded vector using a word embedding table; and run CNN model operations on said encoded vectors to obtain a distributed representation for the encoded vectors. 12. The computer system of claim 11, wherein said at least one processor is further configured to: utilize a set of multiple window sizes to parameterize the CNN model operations on said encoded vectors, each window size corresponds to a convolution matrix of a predetermined dimensionality. 13. The computer system of claim 12, wherein said at least one processor is further configured to: generate a concatenation vector of the resulting vectors for each window size for representation as the distributed representation for encoded variable. 14. The computer system of claim 12, wherein said at least one processor is further configured to: compute a relevance score for the target candidates said using CNN model operations; rank a forward linking, based on the target candidate's relevance score; and provide a forward linking of the entity mentions to identified target candidates. 15. The computer system of claim 14, wherein said at least one processor is further configured to: receive said distributed representations for the encoded vectors; use said RNN to accumulate information about a previous entity mention and target candidate entities, and providing them as the global constraints for the linking process of the current entity mention, wherein said RNN model operations further provide backward linking of the entity mentions to identified target candidates, ranking said backward linking, based on the computed relevance score; and combining forward linking ranks and backward linking ranks, to create a combined linking rank when training and applying the model operations. 16. A computer program product for disambiguating one or more entity mentions in one or more documents, said computer program product comprising: a computer-readable storage medium having computer readable program instructions embodied therewith, the computer readable program instructions executable by at least one processor to cause a computer to perform a computer-implemented method comprising: receiving, at the at least one processor, a set of one or more entity mentions in a document and context data associated with each entity mention; receiving, at the at least one processor, a set of one or more target candidate entities that potentially refers to or describes the entity mentions in the document; running, by the at least one processor, convolutional neural network (CNN) models for identifying and learning local representations associated with each entity mention and target candidate entity and associated context; running recurrent neural networks (RNN) model operations on the at least one processor over the representations of the entity mentions and target candidate entities of the document to capture a topical coherence between the entity mentions and the target candidate entities; and providing a link for each entity mention to link to a respective the target candidates entity in the document based on the identified local features and the topical coherence from the convolutional neural networks and recurrent neural networks. 17. The computer program product of claim 16, wherein said computer readable program instructions configure said at least one processor to cause a computer to further perform a method comprising: encoding multiple variables, each variable comprising a word sequence including an entity mention and associated context, or target candidates, or both the word sequence including an entity mention and associated context and target candidates; transforming each encoded variable into a corresponding encoded vector using a word embedding table; and said at least one processor running CNN model operations on said encoded vectors to obtain a distributed representation for the encoded vectors. 18. The computer program product of claim 17, wherein said computer readable program instructions configure said at least one processor to cause a computer to further perform a method comprising: utilizing a set of multiple window sizes to parameterize the CNN model operations on said encoded vectors, each window size corresponds to a convolution matrix of a predetermined dimensionality; and concatenating the resulting vectors for each window size for representation as the distributed representation for encoded variable as a single concatenation vector. 19. The computer program product of claim 16, wherein said computer readable program instructions configure said at least one processor to cause a computer to further perform a method comprising: computing a relevance score for the target candidates said using CNN model operations; ranking a forward linking, based on the target candidate's relevance score; and providing, by the at least one processor, forward linking of the entity mentions to identified target candidates. 20. The computer program product of claim 16, wherein said computer readable program instructions configure said at least one processor to cause a computer to further perform a method comprising: receiving said distributed representations for the encoded vectors; using said RNN to accumulate information about a previous entity mention and target candidate entities, and providing them as the global constraints for the linking process of the current entity mention, wherein said RNN model operations further provide backward linking of the entity mentions to identified target candidates, ranking said backward linking, based on the computed relevance score; and combining forward linking ranks and backward linking ranks, to create a combined linking rank when training and applying the model operations.