A graph-based framework for information extraction
Name
1124957696-MIT.pdf
Size
2.98 MB
Format
Adobe PDF
Checksum (MD5)
4dc9b3e80e71ca098b078cd07ce8ea79
Author(s)
Qian, Yujie (Computer scientist)
Advisor(s)
Regina Barzilay.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this thesis, we introduce a graph-based framework (GraphIE) that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks -- namely textual, social media and visual information extraction -- shows that GraphlE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-45).
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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