@inproceedings{4b63d721c5e844afb5550f2c22772b39,
title = "Investigation of passage based ranking models to improve document retrieval",
abstract = "Passage retrieval deals with identifying and retrieving small but explanatory portions of a document that answers a user{\textquoteright}s query. In this paper, we focus on improving the document ranking by using different passage based evidence. Several similarity measures were evaluated and a more in-depth analysis was undertaken into the effect of varying specific. We have also explored the notion of query difficulty to understand whether the best performing passage-based approach helps to improve, or not, the performance of certain queries. Experimental results indicate that for the passage level technique, the worst-performing queries are damaged slightly and the those that perform well are boosted for the WebAp collection. However, our rank-based similarity function boosted the performance of the difficult queries in the Ohsumed collection.",
keywords = "Document retrieval, Inverse rank, Passage similarity functions, Passage-based document retrieval, Query difficulty",
author = "Ghulam Sarwar and Colm O{\textquoteright}Riordan and John Newell",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2017 ; Conference date: 01-11-2017 Through 03-11-2017",
year = "2019",
doi = "10.1007/978-3-030-15640-4\_6",
language = "English",
isbn = "9783030156398",
series = "Communications in Computer and Information Science",
publisher = "Springer-Verlag",
pages = "100--117",
editor = "David Aveiro and Kecheng Liu and Joaquim Filipe and Ana Fred and Jorge Bernardino and Ana Salgado and David Aveiro and Dietz, \{Jan L.G.\}",
booktitle = "Knowledge Discovery, Knowledge Engineering and Knowledge Management - 9th International Joint Conference, IC3K 2017, Revised Selected Papers",
}