Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Finding Support Documents with a Logistic Regression Approach

Li, Qi and He, Daqing (2011) Finding Support Documents with a Logistic Regression Approach. In: the First International Workshop on Entity-Oriented Search (EOS), a workshop of ACM SIGIR 2011, 28 July 2011 - 28 July 2011, Beijing China.

[img]
Preview
PDF
Published Version
Available under License : See the attached license file.

Download (284kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

Entity retrieval finds the relevant results for a user’s information needs at a finer unit called “entity”. To retrieve such entity, people usually first locate a small set of support documents which contain answer entities, and then further detect the answer entities in this set. In the literature, people view the support documents as relevant documents, and their findings as a conventional document retrieval problem. In this paper, we will state that finding support documents and that of relevant documents, although sounds similar, have important differences. Further, we propose a logistic regression approach to find support documents. Our experiment results show that the logistic regression method performs significantly better than a baseline system that treat the support document finding as a conventional document retrieval problem.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, Qi
He, Daqingdah44@pitt.eduDAH440000-0002-4645-8696
Date: 28 July 2011
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Publisher: ACM
Event Title: the First International Workshop on Entity-Oriented Search (EOS), a workshop of ACM SIGIR 2011
Event Dates: 28 July 2011 - 28 July 2011
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 22 Jun 2012 15:40
Last Modified: 06 Sep 2019 13:58
URI: http://d-scholarship.pitt.edu/id/eprint/12472

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item