A system that performs natural language processing receives a text corpus that includes a plurality of documents and receives a knowledge base. The system generates a set of document n-grams from the text corpus and considers all n-grams as candidate mentions. The system, for each candidate mention, queries the knowledge base and in response retrieves results. From the results retrieved by the queries, the system generates a search space and generates a joint model from the search space.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of natural language processing comprising: receiving a text corpus comprising a plurality of documents; receiving a knowledge base; generating a set of document n-grams from the text corpus and considering all n-grams as candidate mentions; for each candidate mention, querying the knowledge base and in response retrieving results; from the results retrieved by the queries, generating a search space; and generating a joint model from the search space. 2. The method of claim 1, further comprising: joint training the joint model; and outputting, from the joint model, for a given document, a set of predicted mentions. 3. The method of claim 2, wherein each of the predicted mentions of the set comprises a predicted type and entity. 4. The method of claim 1, wherein the retrieved results are limited to a subset of all retrieved documents from the knowledge base. 5. The method of claim 2, wherein the joint model comprises an inference procedure that predicts a set of non-overlapping candidate tuples. 6. The method of claim 5, wherein each candidate tuple comprises (mention, type, entity), further comprising extracting mention features, link features, type features, and joint features. 7. The method of claim 2, further comprising performing parameter estimation for the joint model. 8. A computer-readable medium having instructions stored thereon that, when executed by a processor, cause the processor to perform natural language processing, the natural language processing comprising: receiving a text corpus comprising a plurality of documents; receiving a knowledge base; generating a set of document n-grams from the text corpus and considering all n-grams as candidate mentions; for each candidate mention, querying the knowledge base and in response retrieving results; from the results retrieved by the queries, generating a search space; and generating a joint model from the search space. 9. The computer-readable medium of claim 8, the natural language processing further comprising: joint training the joint model; and outputting, from the joint model, for a given document, a set of predicted mentions. 10. The computer-readable medium of claim 9, wherein each of the predicted mentions of the set comprises a predicted type and entity. 11. The computer-readable medium of claim 8, wherein the retrieved results are limited to a subset of all retrieved documents from the knowledge base. 12. The computer-readable medium of claim 9, wherein the joint model comprises an inference procedure that predicts a set of non-overlapping candidate tuples. 13. The computer-readable medium of claim 12, wherein each candidate tuple comprises (mention, type, entity), further comprising extracting mention features, link features, type features, and joint features. 14. The computer-readable medium of claim 9, the natural language processing further comprising further comprising performing parameter estimation for the joint model. 15. A natural language processing system comprising: a processor; a storage device coupled to the processor that stores instructions executed by the processor that causes the processor, in response to having access to a text corpus comprising a plurality of documents and a knowledge base, to: generate a set of document n-grams from the text corpus and considering all n-grams as candidate mentions; for each candidate mention, query the knowledge base and in response retrieving results; from the results retrieved by the queries, generate a search space; and generate a joint model from the search space. 16. The system of claim 15, the processor further: joint training the joint model; and outputting, from the joint model, for a given document, a set of predicted mentions. 17. The system of claim 16, wherein each of the predicted mentions of the set comprises a predicted type and entity. 18. The system of claim 16, wherein the joint model comprises an inference procedure that predicts a set of non-overlapping candidate tuples. 19. The system of claim 18, wherein each candidate tuple comprises (mention, type, entity), further comprising extracting mention features, link features, type features, and joint features. 20. The system of claim 15, the processor further performing parameter estimation for the joint model.