Please use this identifier to cite or link to this item: http://repository.iiitd.edu.in/xmlui/handle/123456789/1220
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBisht, Manak-
dc.contributor.authorMutharaju, Vijaya Raghava (Advisor)-
dc.date.accessioned2023-04-19T13:04:00Z-
dc.date.available2023-04-19T13:04:00Z-
dc.date.issued2022-05-
dc.identifier.urihttp://repository.iiitd.edu.in/xmlui/handle/123456789/1220-
dc.description.abstractOpen Information Extraction (Open IE) is the task of extracting triples (subject, predicate, object) from plain text. Tools that perform these extractions are called Open IE extractors. While benchmarks to evaluate the performance of these tools exist, they provide aggregate metrics and do not have room for fine-grained analysis. This benchmark addresses this problem by assigning category labels to test sentences so that it is apparent on what type of sentence the extractor is failing.en_US
dc.language.isoen_USen_US
dc.publisherIIIT-Delhien_US
dc.subjectNatural Language Processingen_US
dc.subjectSentence Clusteringen_US
dc.subjectSentence Embeddingsen_US
dc.subjectTransfer Learningen_US
dc.subjectOpen Information Extraction Benchmarken_US
dc.subjectOpen Information Extractionen_US
dc.titleDevelopment of an open IE benchmarken_US
Appears in Collections:Year-2022

Files in This Item:
File Description SizeFormat 
Manak Bisht.pdf
  Restricted Access
1.63 MBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.