{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/145080"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/145080","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Transforming dependency parses into ternary expressions for enhanced indexing and matching","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Hu, Henry"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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My system is also flexible to changes in the underlying dependency parsers and produces human-readable explanations for each ternary expression it produces."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Transforming dependency parses into ternary expressions for enhanced indexing and matching"]}]}],"canonical_facts":{"dc:contributor.advisor":["Katz, Boris"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Hu, Henry"],"dc:date.accessioned":["2022-08-29T16:31:32Z"],"dc:date.available":["2022-08-29T16:31:32Z"],"dc:date.issued":["2022-05"],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/145080"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Transforming dependency parses into ternary expressions for enhanced indexing and matching"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:59Z"}