1. A method for searching information, comprising:
receiving current query data from a client;
computing, using one or more processors, a plurality of confidence degrees associated with the current query data, wherein the plurality of confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;
selecting, using one or more computer processors, a category based at least in part on the plurality of confidence degrees, the selected category being one of the plurality of categories and to having a confidence degree higher than a confidence degree of another category;
in response to selecting the category based at least in part on the plurality of confidence degrees, searching in the selected category for a search result that corresponds to the current query data; and
returning the search result to the client.
2. The method of claim 1, further comprising: extracting generic attribute features of the current query data, wherein the generic attribute features are used for computing the plurality of confidence degrees of the current query data.
3. The method of claim 2, wherein the generic attribute features comprise a word-level feature that corresponds to a keyword in the current query data.
4. The method of claim 2, wherein the generic attribute features comprise a syntax feature that corresponds to an etymon corresponding to a headword in the current query data.
5. The method of claim 4, wherein extracting the generic attribute features including the syntax feature comprises:
performing part-of-speech tagging of a keyword in the current query data and identifying a phrase including the keyword in the current query data;
obtaining a noun phrase in the current query data based at least in part on results of the part-of-speech tagging and the identification of the phrase;
extracting the etymon associated with the headword of the noun phrase.
6. The method of claim 2, wherein the generic attribute features comprise a semantic feature that corresponds to a semantic tag corresponding to the keyword.
7. The method of claim 6, wherein extracting the generic attribute features including the semantic feature comprises:
identifying the type of the keyword of the current query data; and
acquiring an action verb tag, a product tag, a brand tag, a model tab, or another tag of the keyword according to the identified type.
8. The method of claim 2, wherein the generic attribute features comprise a click feature that corresponds to a clicked category corresponding to the current query data in a click log.
9. The method of claim 8, wherein extracting the generic attribute features including the click feature comprises:
determining whether the current query data is present in the click log, wherein the click log is adapted to store a correspondence relationship between the query data and the click feature;
in the event that the current query data is present in the click log, acquiring a first predetermined number of clicked categories corresponding to the current query data from the preset click log, wherein the clicked categories are categories to which a corresponding product clicked by a user after searching for the query data belongs; and
in the event that the current query data is not present in the click log, inquiring in the click log about clicked query data closest to the current query data, wherein the clicked query data is at the shortest edit distance in the click log from the current query data, and acquiring the first predetermined number of categories corresponding to the clicked query data as the click feature.
10. The method of claim 2, wherein the generic attribute features comprise a selection feature that corresponds to at least a selected category corresponding to the current query data in a selection log.
11. The method of claim 10, wherein extracting the generic attribute features including the selection feature comprises:
determining whether the current query data is present in the selection log, wherein the selection log is adapted to store a correspondence relationship between the query data and the selection feature;
in the event that the current query data is present in the selection log, acquiring a first predetermined number of selected categories corresponding to the current query data from the selection log, wherein the selected categories represent categories associated with the current query data in a mapping table of a search engine; and
in the event that the current query data is not present in the selection log, inquiring in the selection log about selected query data closest to the current query data, wherein, the selected query data is at the shortest edit distance in the selection log from the current query data, and acquiring the first predetermined number of categories corresponding to the selected query data as selection features of the current query data.
12. The method of claim 2, wherein the generic attribute features comprise a session feature that corresponds to a substantially optimal category corresponding to a previous one query preceding the current query data in the same session.
13. The method of claim 12, wherein extracting the generic attribute features including the session feature comprises:
acquiring preceding query data that precedes the current query data in the same session;
acquiring a category to which an optimal categorizing result corresponding to the preceding query data belongs; and
setting the category to which the optimal categorizing result belongs as a session feature of the current query data.
14. The method of claim 1, further comprising determining the plurality of confidence degrees of the current query data based at least in part on the generic attribute features, wherein the determining of the plurality of confidence degrees of the current query data includes:
setting the generic attribute features as given information of a conditional probability model;
calculating a plurality of posterior probabilities that the current query belongs to the respective categories under condition of the given information using the conditional probability model; and
setting the plurality of posterior probabilities as the plurality of confidence degrees.
15. The method of claim 1, further comprising:
identifying selected ones of the plurality of confidence degrees that meet a preset first threshold and their corresponding categories; and
returning the selected ones of the plurality of confidence degrees and their corresponding categories to the client for selecting the selected category for the current query data.
16. The method of claim 1, wherein the chosen category is chosen by a user.
17. The method of claim 1, wherein the chosen category has a maximum calculated confidence degree for the current query data.
18. The method of claim 1, wherein the one or more categories relate to a respective one or more industry categories in which a product associated with a generic attribute feature belongs.
19. The method of claim 1, wherein the selecting of the category based at least in part on a plurality of confidence degrees comprises:
selecting a plurality of categories based at least in part on the plurality of confidence degrees;
providing the plurality of categories to a user; and
receiving an indication from the user of a selection of one of the plurality of categories,
wherein the searching in the selected category for the search result comprises searching in the category corresponding to indication of the selection received from the user.
20. The method of claim 1, wherein the selecting of the category based at least in part on a plurality of confidence degrees comprises:
selecting one or more categories based at least in part on whether the one or more categories are included on a selection log relating to the current query data.
21. A system for searching information, comprising:
one or more processors configured to:
receive current query data from a client;
computing a plurality of confidence degrees associated with the current query data, wherein the plurality of confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;
select a category based at least in part on the plurality of confidence degrees, the selected category being one of the plurality of categories and having a confidence degree higher than a confidence degree of another category;
in response to selecting the category based at least in part on the plurality of confidence degrees, search in the selected category for a search result that corresponds to the current query data; and
return the search result to the client; and
one or more memories coupled to the one or more processors, configured to provide the processors with instructions.
22. A computer program product for searching information, the computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:
causing at least one processor to receive current query data from a client;
causing at least one processor to compute a plurality of confidence degrees associated with the current query data, wherein the confidence degrees respectively indicate a degree of confidence that the current query data is associated with a corresponding one of a plurality of categories;
causing at least one processor to select a category based at least in part on the plurality of confidence degrees, the selected category being one of the plurality of categories and having a confidence degree higher than a confidence degree of another category;
causing at least one processor to search in the selected category for a search result that corresponds to the current query data in response to the category being selected based at least in part on the plurality of confidence degrees; and
causing at least one processor to return the search result to the client.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
What is claimed is:
1. A method for forming an interlayer dielectric film, comprising the step of forming the interlayer dielectric film out of an organicinorganic hybrid film by polymerizing a source material within plasma with a low electron temperature, the source material including an organosilicon compound.
2. The method of claim 1, wherein the organosilicon compound has an, SiOSi bond.
3. The method of claim 1, wherein the organosilicon compound has an SiOR bond, where R is selected from the group consisting of alkyl, allyl and aryl groups.
4. The method of claim 1, wherein the organosilicon compound is selected from the group consisting of hex-amethyldisiloxane, methyltrialkoxysilane, dimethyldialkoxysilane, trimethylalkoxysilane, tetramethylsilane and a mixture thereof.
5. The method of claim 1, wherein the plasma polymerization-is conducted at a temperature of 350 C. or more.
6. The method of claim 1, wherein the plasma polymerization is conducted within an environment in which an oxidizing agent is contained.
7 The method of claim 1, wherein the oxidizing agent is nitrogen monoxide.
8. The method of claim 1, wherein the mass of the oxidizing agent contained is equal to or less than its chemical equivalent for the organosilicon compound.
9. The method of claim 1, wherein the interlayer diaelectric film is formed by heating the organicinorganic hybrid film to a temperature higher than a temperature set for the plasma polymerization.
10. The method of claim 1, wherein an organic compound is contained in the source material.
11. The method of claim 1, wherein the organicinorganic hybrid film has an organosilicon bend.
12. The method of claim 1, wherein the plasma polymerization is conducted within an environment, in which a vacuum has a pressure of 650 Pa or more containing a dilute gas.
13. The method of claim 1, wherein the dilute gas is nitrogen gas.
14. The method of claim 1, wherein the interlayer dielectric film, having a porous structure is formed by the heating process.
15. The method of claim 1, wherein the organic compound is selected from the group consisting of methane, ethane, propane, butane, benzene, hexane or decahydronaphthalene.