NDLI: Concept-based document readability in domain specific information retrieval

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Concept-based document readability in domain specific information retrieval

Content Provider	ACM Digital Library
Author	Yan, Xin Song, Dawei Li, Xue
Abstract	Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document's readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users' readability ratings over four traditional readability measures.
Starting Page	540
Ending Page	549
Page Count	10
File Format	PDF
ISBN	1595934332
DOI	10.1145/1183614.1183692
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Document readability Document ranking Document scope and cohesion
Content Type	Text
Resource Type	Article

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