Methods, systems, and apparatus for mining feedback are described. A set of one or more lexical patterns associated with one or more of a suggestion and a defect report are determined and the set of one or more lexical patterns are matched against a plurality of feedback items to generate a distance learning training set. A distance learning technique is applied to the distance learning training set to generate a distance learning model and the distance learning model is used to identify one or more candidate feedback items of the plurality of feedback items, each of which is one or more of a candidate suggestion and a candidate defect report.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for processing a plurality of feedback items, the system comprising: a feedback detection module comprising one or more hardware processors, the feedback detection module configured to: determine a set of one or more lexical patterns associated with one or more of a suggestion and a defect report; match the set of one or more lexical patterns against the plurality of feedback items to generate a distance learning training set; apply a distance learning technique to the distance learning training set to generate a distance learning model; and using the distance learning model to identify one or more candidate feedback items of the plurality of feedback items that are one or more of a candidate suggestion and a candidate defect report. 2. The system of claim 1, wherein the set of one or more lexical patterns are determined by applying a first machine-learning algorithm to a seed set of feedback items. 3. The system of claim 2, wherein the first machine-learning algorithm is a bootstrapping algorithm. 4. The system of claim 1, wherein the feedback detection module is further configured to obtain one or more of manually labeled relevant candidate suggestions and manually labeled relevant candidate defect reports. 5. The system of claim 4, wherein the feedback detection module is further configured to apply a first machine-learning technique to a subset of the relevant candidate suggestions to generate a classifying model for suggestions and apply a second machine-learning technique to a subset of the relevant candidate defect reports to generate a classifying model for defect reports. 6. The system of claim 5, wherein the feedback detection module is further configured to classify each of one or more of the relevant candidate suggestions as one of a valid suggestion and an invalid suggestion using the classifying model for suggestions and classify each of one or more of the relevant candidate defect reports as one of a valid defect report and an invalid defect report using the classifying model for defect reports. 7. The system of claim 1, further comprising a paraphrasing module configured to identify each of one or more snippets as one or more of a relevant suggestion snippet and a relevant defect snippet. 8. The system of claim 7, the paraphrasing module further configured to: apply a first snippet extraction machine-learning algorithm to a subset of the relevant suggestion snippets to generate a suggestion snippet extraction model; and apply a second snippet extraction machine-learning algorithm to a subset of the relevant defect snippets to generate a defect snippet extraction model. 9. The system of claim 8, the paraphrasing module further configured to: apply the suggestion snippet extraction model to valid suggestions to generate one or more valid suggestion snippets; and apply the defect snippet extraction model to valid defect reports to generate one or more valid defect report snippets. 10. The system of claim 9, the paraphrasing module further configured to group the valid suggestion snippets and the valid defect report snippets by type into one or more groups. 11. The system of claim 10, further comprising a gist module configured to apply a topic modeling technique to identify a top k snippets for each group of the one or more groups of valid suggestion snippets and valid defect report snippets based on a probability of co-occurrence with other topics in the group. 12. The system of claim 11, the gist module further configured to generate a gist for each group that summarizes a topic for the group. 13. The system of claim 12, the gist module further configured to apply statistical measures and linguistic processing to create an explicative sentence that summarizes the topic associated with each group. 14. A method for processing a plurality of feedback items, the method comprising: determining a set of one or more lexical patterns associated with one or more of a suggestion and a defect report; matching the set of one or more lexical patterns against the plurality of feedback items to generate a distance learning training set; applying a distance learning technique to the distance learning training set to generate a distance learning model; and using the distance learning model to identify one or more candidate feedback items of the plurality of feedback items that are one or more of a candidate suggestion and a candidate defect report. 15. The method of claim 14, wherein the set of one or more lexical patterns are manually determined. 16. The method of claim 14, wherein the set of one or more lexical patterns are determined by applying a first machine-learning algorithm to a seed set of feedback items. 17. The method of claim 14, further comprising manually labeling each of the one or more candidate feedback items as one or more of a relevant candidate suggestion and a relevant candidate defect report. 18. The method of claim 17, further comprising applying a first machine-learning technique to a subset of the relevant candidate suggestions to generate a classifying model for suggestions and applying a second machine-learning technique to a subset of the relevant candidate defect reports to generate a classifying model for defect reports. 19. The system of claim 18, further comprising classifying each of one or more of the relevant candidate suggestions as one of a valid suggestion and an invalid suggestion using the classifying model for suggestions and classifying each of one or more of the relevant candidate defect reports as one of a valid defect report and an invalid defect report using the classifying model for defect reports. 20. A non-transitory computer-readable medium embodying instructions that, when executed by a processor, perform operations comprising: determining a set of one or more lexical patterns associated with one or more of a suggestion and a defect report; matching the set of one or more lexical patterns against the plurality of feedback items to generate a distance learning training set; applying a distance learning technique to the distance learning training set to generate a distance learning model; and using the distance learning model to identify one or more candidate feedback items of the plurality of feedback items that are one or more of a candidate suggestion and a candidate defect report.