Learning a semantic database from unstructured text
Name
893679084-MIT.pdf
Description
Full printable version
Size
1.63 MB
Format
Adobe PDF
Checksum (MD5)
f8d6e8f7f71e3995a14c4c3a62510eae
Author(s)
Dhandhania, Keshav
Advisor(s)
Tommi Jaakkola.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
Abstract
In this paper, we aim to learn a semantic database given a text corpus. Specifically, we focus on predicting whether or not a pair of entities are related by the hypernym relation, also known as the 'is-a' or 'type-of' relation. We learn a neural network model for this task. The model is given as input a description of the words and the context from the text corpus in which a pair of nouns (entities) occur. In particular, among other things the description includes pre-trained embeddings of the words. We show that the model is able to predict hypernym noun pairs even though the dataset includes many incorrectly labeled noun pairs. Finally, we suggest ways to improve the dataset and the method.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June 2014.
24
"May 23, 2014." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 35-38).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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