Open Information Extraction or Open IE is a paradigm which enables extraction of relational tuples from text without pre-specifying relations. Most of the work in Open IE has been done for English. In this thesis we leverage the Open IE tools present in English to generate data in a non-English language by using cross lingual projection. This data can be used to train models capable of extracting relational tuples in multiple
languages. Universal Dependencies are used to generate features for these models that can be used across multiple languages.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Information storage and retrieval systems
Subject (authority = local)
Topic
Open information extraction
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9349
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (38 pages) : illustrations
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Shardul Naithani
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
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