We have approached the Prior art Task using the BM25F retrieval model considering four different fields for each XML document: title, abstract, description and claims. That is, given a document in the indexing process, the index is fed with the content of the document for each one of these fields.

We have used three different indexes, one for each language. Each index contains only the documents of this language. In the case of fields in a language different to the one of the document (for example, the title can be in different languages), the field is ignored. Pre-processing for feeding the indexes included stemming and stop words removal. It is important to remark that we indexed patent documents instead of patent versions. That is, we joined the fields of all the versions of a patent, and then we added the terms of this patent to the corresponding index. In this way, the document associated to a term in a field is a patent instead of a version.

Given a topic, the language of the topic (information that is available in the given topic) is used for deciding which index (and only this index) is going to be used.

We made use of language models for selecting the terms of the query. In fact, we built two different language models given a topic document: one language model of the whole indexed collection, and another one for the given topic document. Each of these language models is built taking into account each one of the considered fields.

Then, we computed the Kullback-Leibler divergence (KLD) between the language model of the topic and the one of the collection (at the level of each field). Thus, we can sort the terms of the topic according to their divergence. The idea beyond using KLD is that those terms with a high frequency in the document but a low frequency in the collection would be the most representative ones of the topic document. We think that these most representative terms will be the best ones for retrieving relevant patents.

We took a look at the average length of each field in the collection and we performed several experiments that helped us to decide the amount of words to be used for each field in a query. These words are selected taking the most discriminative ones of each field (according to KLD).

Finally, we have also given different boost values to each field, in order to increase the importance of the terms of a certain field in a query. 
