| 44998 |
Creator |
56412434212aaec1af1a675d5f017f1e |
| 44998 |
Creator |
a057dc830901e1d749423ba53785e145 |
| 44998 |
Creator |
b718ee3619ebe0ed07678edb8b16f80f |
| 44998 |
Creator |
ext-4f79cf26d4b73801ce458ce9dffe9267 |
| 44998 |
Creator |
ext-d78da3531a4878c0b17a803ac4c4bdb2 |
| 44998 |
Date |
2016-01-22 |
| 44998 |
Is Part Of |
p03029743 |
| 44998 |
Is Part Of |
repository |
| 44998 |
abstract |
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. |
| 44998 |
authorList |
authors |
| 44998 |
presentedAt |
ext-f4ee057a13de83412d3e3c03334d7def |
| 44998 |
status |
peerReviewed |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385743 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385744 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385745 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385746 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385747 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/385748 |
| 44998 |
uri |
http://data.open.ac.uk/oro/document/400426 |
| 44998 |
volume |
9460 |
| 44998 |
type |
AcademicArticle |
| 44998 |
type |
Article |
| 44998 |
label |
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. |
| 44998 |
label |
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. |
| 44998 |
Publisher |
ext-1c5ddec173ca8cdfba8b274309638579 |
| 44998 |
Title |
A Sequential Latent Topic-based Readability Model for Domain-Specific Information
Retrieval. |
| 44998 |
in dataset |
oro |