2026-09-19T13:29:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006412026-06-06T00:55:41Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Regina Barzilay.
Narayanan, Deepak
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T20:01:22Z
2016-01-04T20:01:22Z
2015
2015
http://hdl.handle.net/1721.1/100641
933236372
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).
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.
by Deepak Narayanan.
M.Eng.
69 pages
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.
Building and processing a dataset containing articles related to food adulteration
Thesis
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