- document fields used for training the classifier: all, except ICO codes and legal status information
- document fields used as input to the classifier: all, all, except ICO codes and legal status information
- pre-processing of documents/topics: similar as for the prior art task, without concept tagging
- ranking model: similar as the prior art task
- classification system: KNN and additional post-ranking
- cross-language and language specific methods used: similar as for the prior art task
- any other notes of relevance to your work and this track: We used an instance-based classifier (KNN), the classification is therefore expressed as a search problem. The search itself is realized with a system similar to the prior art task, without conceptual tagging (which presently requires IPC classes for term disambiguation) and, of courese, without classification information. A post ranking is realized based on co-classifications  as observed in the collection. 

We assumed that the evaluation will be based on MAP and gave the complete distribution of all IPC subclasses for the patent to be classified. 
