Building and processing a dataset containing articles related to food adulteration
Name
933236372-MIT.pdf
Description
Full printable version
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571.14 KB
Format
Adobe PDF
Checksum (MD5)
ab1e1476f07b9e4b68a670c9d81d61e8
Author(s)
Narayanan, Deepak
Advisor(s)
Regina Barzilay.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I explored the problem of building a dataset containing news articles related to adulteration, and curating this dataset in an automated fashion. In particular, we looked at food-adulterant co-existence detection, query reforumulation, and entity extraction and text deduplication. All proposed algorithms were implemented in Python, and performance was evaluated on multiple datasets. Methods described in this thesis can be generalized to other applications as well.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 69).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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