There is provided a search system comprising a statistical model trained on text associated with a piece of content. The text associated with the piece of content is drawn from a plurality of different data sources. The system is configured to receive text input and generate using the statistical model an estimate of the likelihood that the piece of content is relevant given the text input. A corresponding method is also provided.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more processors; and program instructions, wherein execution of the program instructions by the one or more processors causes the one or more processors to: receive text input; generate, using a statistical model, an estimate of a likelihood that a piece of content is relevant given the text input, wherein the statistical model is trained on text associated with the piece of content, wherein the text associated with the piece of content is drawn from a plurality of different data sources. 2. The system of claim 1, wherein the statistical model is trained on one or more features extracted from the text associated with the piece of content, and wherein execution of the program instructions by the one or more processors causes the one or more processors to: extract the one or more features from the text input, and query the statistical model with each of the one or more features of the text input to generate the estimate of the likelihood that the piece of content is relevant given the text input. 3. The system of claim 1, wherein execution of the program instructions by the one or more processors causes the one or more processors to: generate an estimate of the likelihood that each piece of content of a plurality of pieces of content is relevant given the text input using a statistical model associated with each piece of content selected from a plurality of statistical models trained on text associated with the plurality of pieces of content. 4. The system of claim 3, wherein execution of the program instructions by the one or more processors causes the one or more processors to: rank the plurality of pieces of content by relevance and determine one or more of the most relevant pieces of content. 5. The system of claim 4, wherein execution of the program instructions by the one or more processors causes the one or more processors to output at least one representation of each of the one or more most relevant pieces of content. 6. The system of claim 4, wherein execution of the program instructions by the one or more processors causes the one or more processors to output the one or more most relevant pieces of content. 7. The system of claim 1, wherein the piece of content is a particular entity. 8. The system of claim 1, wherein the piece of content is associated with a particular entity. 9. The system of claim 4, wherein at least one piece of content is associated with a particular entity and wherein execution of the program instructions by the one or more processors causes the one or more processors to output at least one representation of one or more entities associated with the one or more most relevant pieces of content. 10-12. (canceled) 13. The system of claim 3, wherein execution of the program instructions by the one or more processors causes the one or more processors to: classify each of the plurality of statistical models trained on text by content type; classify the text input by content type; and determine a subset of the plurality of statistical models which are of the same content type as the text input. 14. (canceled) 15. The system of claim 1, wherein execution of the program instructions by the one or more processors causes the one or more processors to: compare a portion of the text input to a first language model trained on natural language text; compare the portion of the text input to a second language model trained on conversational text; and classify the portion of text input as natural language or conversational. 16. (canceled) 17. A method for determining whether a piece of content is relevant to text input, the method comprising: receiving text input; generating, using a statistical model trained on text associated with a piece of content, an estimate of the likelihood that the piece of content is relevant given the text input, wherein the text associated with the piece of content is drawn from a plurality of different data sources. 18. The method of claim 17, further comprising: extracting, using one or more processors, one or more features from the text input; and querying, using one or more processors, the statistical model with each of the one or more feature of the text input to generate an estimate of the likelihood that the piece of content is relevant given the text input. 19-33. (canceled) 34. A non-transitory computer-readable medium containing program instructions for determining whether a piece of content is relevant to text input, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to: receive text input; generate, using a statistical model trained on text associated with a piece of content, an estimate of the likelihood that the piece of content is relevant given the text input, wherein the text associated with the piece of content is drawn from a plurality of different data sources. 35. The non-transitory computer-readable medium of claim 34, wherein the text input is not input into the system by a user. 36. The non-transitory computer-readable medium of claim 34, wherein execution of the program instructions by the one or more processors causes the one or more processors to: receive non-textual evidence; and generate using a statistical model that is trained on non-textual data an estimate of the likelihood that the piece of content is relevant given the non-textual evidence. 37. The non-transitory computer-readable medium of claim 34, wherein execution of the program instructions by the one or more processors causes the one or more processors to: classify each of a plurality of statistical models trained on text by content type; classify the text input by content type; and determine a subset of the plurality of statistical models which are of the same content type as the text input. 38. The non-transitory computer-readable medium of claim 37, wherein the statistical model is trained on one or more features extracted from the text associated with the piece of content, wherein execution of the program instructions by the one or more processors causes the one or more processors to: extract the one or more features from the text input, and query each of the statistical models of the subset of statistical models with each of the one or more features of the text input to generate an estimate of the likelihood that each piece of content associated with each model of the subset of models is relevant given the text input. 39. The non-transitory computer-readable medium of claim 34, wherein execution of the program instructions by the one or more processors causes the one or more processors to: compare a portion of the text input to a first language model trained on natural language text; compare the portion of the text input to language model trained on conversational text; classify the portion of text input as natural language or conversational. 40. The non-transitory computer-readable medium of claim 39, wherein execution of the program instructions by the one or more processors causes the one or more processors to discard the portion of text input if it is classified as conversational. 41. The non-transitory computer-readable medium of claim 34, wherein the piece of content is a particular entity.