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An information retrieval method includes pre-processing a set of historical query information and processing a user query. Pre-processing a set of historical query information includes determining a plurality of semantic patterns based on a plurality of queries in the set of historical query informa

An information retrieval method includes pre-processing a set of historical query information and processing a user query. Pre-processing a set of historical query information includes determining a plurality of semantic patterns based on a plurality of queries in the set of historical query information; establishing correspondence relationships between the plurality of semantic patterns and a plurality of filtering and ranking operations. Processing a user query comprises receiving the user query; retrieve a plurality of results in response to the user query; determining a semantic pattern that corresponds to the user query; determining a set of filtering and ranking operations that corresponds to the semantic pattern based on the correspondence relationships; and performing the set of filtering and ranking operations on the plurality of results to generate a set of filtered and ranked results.

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1. An information retrieval method, comprising: pre-processing a set of historical query information, comprising: determining a plurality of semantic patterns based on a plurality of queries in the set of historical query information, wherein the determining of the plurality of semantic patterns com

1. An information retrieval method, comprising: pre-processing a set of historical query information, comprising: determining a plurality of semantic patterns based on a plurality of queries in the set of historical query information, wherein the determining of the plurality of semantic patterns comprises: determining whether a number of times a sematic pattern appears in the set of historical query information exceeds a predetermined threshold; andin the event that the number of times the sematic pattern appears in the set of historical query information exceeds the predetermined threshold, selecting the semantic pattern;establishing correspondence relationships between the plurality of semantic patterns and a plurality of filtering and ranking operations, wherein establishing correspondence relationships comprises: determining user behaviors corresponding to the plurality of semantic patterns based on the set of historical query information;determining a plurality of user intention attributes of the plurality of semantic patterns based on the user behaviors, wherein the plurality of user intention attributes include a degree of ambiguity attribute, an authority requirement attribute, a time efficiency requirement attribute, a location requirement attribute, a volume attribute, or any combination thereof;determining the plurality of filtering and ranking operations based on the plurality of user intention attributes; andstoring correspondence relationships between the plurality of filtering and ranking operations and semantic patterns based on the user behavior sets and user intention attributes; andprocessing a user query made by a user, comprising: receiving the user query;retrieve a plurality of results in response to the user query;determining a semantic pattern that corresponds to the user query;determining a set of filtering and ranking operations that corresponds to the semantic pattern based on the correspondence relationships that are established during the pre-processing; andperforming the set of filtering and ranking operations on the plurality of results to generate a set of filtered and ranked results. 2. The method of claim 1, wherein determining the plurality of semantic patterns based on the plurality of queries in the historical query information includes: identifying a plurality of semantic tag sets for the plurality of queries, each semantic tag set comprising a plurality of semantic tags that characterizes a plurality of query terms in a corresponding query;determining possible semantic patterns that correspond to the plurality of queries in the set of historical records based on the plurality of semantic tag sets. 3. The method of claim 2, further comprising: computing entropy of the semantic pattern; anddiscarding the semantic pattern if its entropy does not meet a predetermined threshold value. 4. The method of claim 1, wherein the plurality of user intention attributes include a degree of ambiguity attribute. 5. The method of claim 1, wherein the plurality of user intention attributes include an authority requirement attribute. 6. The method of claim 1, wherein the plurality of user intention attributes include a time efficiency requirement attribute. 7. The method of claim 1, wherein the plurality of user intention attributes include a location requirement attribute. 8. The method of claim 1, wherein the plurality of user intention attributes include a volume attribute. 9. An information retrieval system, comprising: one or more processors configured to: pre-process a set of historical query information, comprising: determining a plurality of semantic patterns based on a plurality of queries in the set of historical query information, wherein the determining of the plurality of semantic patterns comprises: determining whether a number of times a sematic pattern appears in the set of historical query information exceeds a predetermined threshold; andin the event that a number of times a sematic pattern appears in the set of historical query information exceeds the predetermined threshold, selecting the semantic pattern;establishing correspondence relationships between the plurality of semantic patterns and a plurality of filtering and ranking operations, wherein establishing correspondence relationships comprises: determining user behaviors corresponding to the plurality of semantic patterns based on the set of historical query information;determining a plurality of user intention attributes of the plurality of semantic patterns based on the user behaviors, wherein the plurality of user intention attributes include a degree of ambiguity attribute, an authority requirement attribute, a time efficiency requirement attribute, a location requirement attribute, a volume attribute, or any combination thereof;determining the plurality of filtering and ranking operations based on the plurality of user intention attributes; andstoring correspondence relationships between the plurality of filtering and ranking operations and semantic patterns based on the user behavior sets and user intention attributes; andprocess a user query made by a user, comprising: receiving the user query;retrieving a plurality of results in response to the user query;determining a semantic pattern that corresponds to the user query;determining a set of filtering and ranking operations that corresponds to the semantic pattern based on the correspondence relationships that are established during the pre-processing; and performing the set of filtering and ranking operations on the plurality of results to generate a set of filtered and ranked results; andone or more memories coupled to the processors, configured to provide the processors with instructions. 10. The system of claim 9, wherein determining the plurality of semantic patterns based on the plurality of queries in the historical query information includes: identifying a plurality of semantic tag sets for the plurality of queries, each semantic tag set comprising a plurality of semantic tags that characterizes a plurality of query terms in a corresponding query;determining possible semantic patterns that correspond to the plurality of queries in the set of historical records based on the plurality of semantic tag sets. 11. The system of claim 10, further comprising: computing entropy of the semantic pattern; anddiscarding the semantic pattern if its entropy does not meet a predetermined threshold value. 12. The system of claim 9, wherein the plurality of user intention attributes include a degree of ambiguity attribute. 13. The system of claim 9, wherein the plurality of user intention attributes include an authority requirement attribute. 14. The system of claim 9, wherein the plurality of user intention attributes include a time efficiency requirement attribute. 15. The system of claim 9, wherein the plurality of user intention attributes include a location requirement attribute. 16. The system of claim 9, wherein the plurality of user intention attributes include a volume attribute. 17. A computer program product for inferring a characteristic of an individual, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for: pre-processing a set of historical query information, comprising: determining a plurality of semantic patterns based on a plurality of queries in the set of historical query information, wherein the determining of the plurality of semantic patterns comprises: determining whether a number of times a sematic pattern appears in the set of historical query information exceeds a predetermined threshold; andin the event that the number of times the sematic pattern appears in the set of historical query information exceeds the predetermined threshold, selecting the semantic pattern;establishing correspondence relationships between the plurality of semantic patterns and a plurality of filtering and ranking operations, wherein establishing correspondence relationships comprises: determining user behaviors corresponding to the plurality of semantic patterns based on the set of historical query information;determining a plurality of user intention attributes of the plurality of semantic patterns based on the user behaviors, wherein the plurality of user intention attributes include a degree of ambiguity attribute, an authority requirement attribute, a time efficiency requirement attribute, a location requirement attribute, a volume attribute, or any combination thereof;determining the plurality of filtering and ranking operations based on the plurality of user intention attributes; andstoring correspondence relationships between the plurality of filtering and ranking operations and semantic patterns based on the user behavior sets and user intention attributes; andprocessing a user query made by a user, comprising: receiving the user query;retrieving a plurality of results in response to the user query;determining a semantic pattern that corresponds to the user query;determining a set of filtering and ranking operations that corresponds to the semantic pattern based on the correspondence relationships that are established during the pre-processing; andperforming the set of filtering and ranking operations on the plurality of results to generate a set of filtered and ranked results. 18. The method of claim 1, wherein the determining of the plurality of filtering operations comprises selecting specific results based on the plurality of user intention attributes.