ORCAS-I

It allows you to train your algorithm on various types of user intents. Those intents are initially taken from Broder's (2002) classification: informational, navigational and transactional.

We also refined this classification and added two subcategories inside the informational category: factual and instrumental. If the intent did not get any label inside the informational category it was classified as abstain. ORCAS-I consists of the following files: a) ORCAS-I-18M.tsv A complete ORCAS data set which contains 18 million unique query-urls pairs. dataset size: 18,823,602 unique queries: 10,405,339 unique URLs: 1,422,029 unique domains: 241,199 b) ORCAS-I-2M.tsv A 2M subset of ORCAS-I-18M.tsv that we used for our experiments with different machine learning algorithms. dataset size: 2,000,000 unique queries: 1,796,652 unique URLs: 618,679 unique domains: 126,001 Both ORCAS-I-18M and ORCAS-I-2M contain the following columns: qid : the id of the query query : the text of the query url : the url that the user clicked did : the document from TREC deep learning track that the url leads to level_1 : first level of annotation which has three top level categories: informational, navigational and transactional level_2 : second level of annotation (only classifies according to factual and instrumental categories, so all the other intents in the column are classified as abstain) label : final intent label.