Transforming dependency parses into ternary expressions for enhanced indexing and matching
Name
Hu-henryhu-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
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4.14 MB
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Adobe PDF
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121c1924754816fd58a7b1051d4067d4
Author(s)
Hu, Henry
Advisor(s)
Katz, Boris
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
Advancements in dependency parsing allow machines to quickly and accurately analyze natural language sentences; however, these parses often require non-trivial manipulation to be useful for many applications. This thesis describes Astroparse, a system for producing ternary expression (subject–relation–object triple) parses by building on existing third-party dependency parsers. I present a design which uses a previously-studied training-example framework with additional augmentations to expand its parsing abilities. I analyze some ways that dependency parse representations fail to capture important relationships in sentences and present algorithms to recover ternary expressions despite those failures. I evaluate my system by examining its outputted ternary expressions manually as well as by qualitatively analyzing its learned transformations. On sentences from high-quality articles in Wikipedia, Astroparse achieves an average precision of up to 93.4% and an estimated recall of about 88.1%, and recovers an average of 35.3% more relations than raw dependency parses alone. My system is also flexible to changes in the underlying dependency parsers and produces human-readable explanations for each ternary expression it produces.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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