2026-09-19T19:07:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/356092022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David R. Karger.
Shen, Yuan Kui
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2007-01-10T16:47:43Z
2007-01-10T16:47:43Z
2005
2006
http://hdl.handle.net/1721.1/35609
75289843
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.
Includes bibliographical references (p. 149-152).
As the amount of information on the World Wide Web grows, there is an increasing demand for software that can automatically process and extract information from web pages. Despite the fact that the underlying data on most web pages is structured, we cannot automatically process these web sites/pages as structured data. We need robust technologies that can automatically understand human-readable formatting and induce the underlying data structures. In this thesis, we are focused on solving a specific facet of this general unsupervised web information extraction problem. Structured data can appear in diverse forms from lists to trees to even semi-structured graphs. However, much of the information on the web appears in a flat format we call "records". In this work, we will describe a system, MURIEL, that uses supervised and unsupervised learning techniques to effectively extract records from webpages.
by Yuan Kui Shen.
S.M.
152 p.
10456463 bytes
10997527 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Automatic record extraction for the World Wide Web
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="640b87b9-9d56-4153-97d5-fdc02c868277">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Automatic record extraction for the World Wide Web</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2006</PublicationDate>
<Authors>
<Author>
<DisplayName>Shen, Yuan Kui</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>As the amount of information on the World Wide Web grows, there is an increasing demand for software that can automatically process and extract information from web pages. Despite the fact that the underlying data on most web pages is structured, we cannot automatically process these web sites/pages as structured data. We need robust technologies that can automatically understand human-readable formatting and induce the underlying data structures. In this thesis, we are focused on solving a specific facet of this general unsupervised web information extraction problem. Structured data can appear in diverse forms from lists to trees to even semi-structured graphs. However, much of the information on the web appears in a flat format we call "records". In this work, we will describe a system, MURIEL, that uses supervised and unsupervised learning techniques to effectively extract records from webpages.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>