Prior Art Candidate Search Task (PAC): 

[Run01]

§  document fields used for index creation
-- generic Spinque index was created
-- for querying the following fields were used:
--- IPCR classification,
--- snowball stemmed text of the whole document, with English stopword removal
--- relation between patents and patent-documents

§  (number of) created indexes, index languages
-- Spinque indexer (based on the MonetDB relational database), which is a single index
-- the relations in the index have been specified with the XQuery languange

§  document fields used for query generation
-- a generic Spinque index was created for the topic documents
-- the snowball stemmed text of the whole document, with English stopword removal was used for query generation,
   top 26 words were taken based on TF-IDF score within the topic-collection
-- the ipcr-classes of the topic document

§  pre-processing of documents/topics
-- the topics were pre-processed so that the format was similar to the clef-ip corpus (the ipcr-classes were differently formatted)
   This allowed us to use the same indexer configuration for both the corpus as the topics

§  indexing, retrieving systems
-- the Spinque indexer, and the Spinque strategy builder (both based on MonetDB)

§  ranking model
-- BM25

§  use of cross-language and language specific methods you’ve made use of
-- we tried automatic topic translation, and searching in specific languages, but did not have enough 
   time to improve scores over more simple approaches

§  any other notes of relevance to your work and this track
-- All strategies were created with a graphical query builder. We have tried about 20 different strategies. 
   no coding or scripting was necessary to generate the results. 

o    Classification Task (CLS): 

[Run01]

§  document fields used for training the classifier
-- no training was done. a generic Spinque index was created with thesame configuration as for the PAC task.

§  document fields used as input to the classifier
-- only the text of the topic document (top 26 terms, based on TF-IDF score)

§  pre-processing of documents/topics
-- same index as used for the PAC task

§  ranking model
-- BM25

§  classification system
-- use of the relation patent-document -> IPCR-classification

§  cross-language and language specific methods used
-- none

§ any other notes of relevance to your work and this track
-- The results have been generated by propagating the scores of the documents to 
   the ipcr-classifications attached to the documents (and thereby summing the 
   scores for each ipcr-class). The propagation of scores is implicit in the Spinque 
   software.
