@inproceedings{2a4574ee089948a8b0827928f5649998,
title = "Document retrieval using entity-based language models",
abstract = "We address the ad hoc document retrieval task by devising novel types of entity-based language models. The models utilize information about single terms in the query and documents as well as term sequences marked as entities by some entity-linking tool. The key principle of the language models is accounting, simultaneously, for the uncertainty inherent in the entity-markup process and the balance between using entity-based and term-based information. Empirical evaluation demonstrates the merits of using the language models for retrieval. For example, the performance transcends that of a state-of-the-art term proximity method. We also show that the language models can be effectively used for cluster-based document retrieval and query expansion.",
keywords = "Document retrieval, Entity-based language models",
author = "Hadas Raviv and Oren Kurland and David Carmel",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 ; Conference date: 17-07-2016 Through 21-07-2016",
year = "2016",
month = jul,
day = "7",
doi = "https://doi.org/10.1145/2911451.2911508",
language = "الإنجليزيّة",
series = "SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval",
pages = "65--74",
booktitle = "SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}