##Description of run_lrmTACDnoTFIDF_PAC_small:
Retrieval step: 
	
In a first step we extracted the complete IPC codes (up 'till subgroup level) from all the patent documents in the CLEF-IP 2010 corpus. These sections were saved in the Lemur index
format (lowercase, no punctuation) with the patent IDs as DOCIDs. The topic documents were indexed using Lemur's BuildIndex function. We retrieved 100 patents per topic using Lemur's RetEval function, 
retrieval model TF-IDF.
	
Re-ranking step:

Using logistic regression model we aim to improve the ranking of the retrieved documents on the basis of the information present in the different sections. The features used for this model were extracted as follows:
For each query_document pair from the training result set, we extracted the title, abstract, claims and description sections (if present) from both the query and the retrieved document. We then calculated the cosine similarity between the sections of the respective documents. For each query_document pair we got a vector with 5 features: query_document_ID cosine_similarity_titles cosine_similarity_abstracts cosine_similarity_claims cosine_similarity_descriptions  We used the lrm function from the Design package in R for training models based on logistic regression. We then used this model (trained on the training data) to predict an alternative ranking for the results in the result set from the topic queries.

