The Open UniversitySkip to content

A Sequential Latent Topic-based Readability Model for Domain-Specific Information Retrieval.

Zhang, Wenya; Song, Dawei; Zhang, Peng; Zhao, Xiaozhao and Hou, Yuexian (2016). A Sequential Latent Topic-based Readability Model for Domain-Specific Information Retrieval. In: Information Retrieval Technology, Springer, pp. 241–252.

Full text available as:
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (528kB) | Preview
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


In domain-specific information retrieval (IR), an emerging problem is how to provide different users with documents that are both relevant and readable, especially for the lay users. In this paper, we propose a novel document readability model to enhance the domain-specific IR. Our model incorporates the coverage and sequential dependency of latent topics in a document. Accordingly, two topical readability indicators, namely Topic Scope and Topic Trace are developed. These indicators, combined with the classical Surface-level indicator, can be used to rerank the initial list of documents returned by a conventional search engine. In order to extract the structured latent topics without supervision, the hierarchical Latent Dirichlet Allocation (hLDA) is used. We have evaluated our model from the user-oriented and system-oriented perspectives, in the medical domain. The user-oriented evaluation shows a good correlation between the readability scores given by our model and human judgments. Furthermore, our model also gains significant improvement in the system-oriented evaluation in comparison with one of the state-of-the-art readability methods.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 Springer
ISBN: 3-319-28939-X, 978-3-319-28939-7
ISSN: 0302-9743
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 44998
Depositing User: Dawei Song
Date Deposited: 02 Feb 2016 15:09
Last Modified: 07 Dec 2018 22:54
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU