- Using semantic features for learning-to-rank in Text Retrieval.
- Made for a course project of the 2018/2019 course Information Retrieval at Radboud University.
- Built atop the ChatNoir web search engine developed by University of Weimar researchers.
- For a set of reference queries, retrieves the 100 top results from ChatNoir.
- Re-ranks them using a machine-learning model trained on hand-labelled relevant documents.
- Compares different query-document representations for this purpose, including word2vec similarity and a new feature representation based on phrasal semantics.
- Apply for a ChatNoir API key and once you have it, add it to this file.
- Download the TREC web track adhoc qrels and queries for 2010, 2011 and 2012 from the TREC page and put them into the respective paths, e.g. TREC 2010 should go into here.
- Download the pre-trained Google News word embeddings from here, unzip them and add them to this folder.
- You're good to go, run the entry script.