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dc.contributor.authorLiang, Jason
dc.contributor.authorMcGuinness, Deborah
dc.date.accessioned2022-02-15T17:29:40Z
dc.date.available2022-02-15T17:29:40Z
dc.date.issued2021-01-02
dc.identifier.other10
dc.identifier.urihttps://arxiv.org/pdf/2102.00651.pdf
dc.description.abstractCommonsense knowledge has proven to be beneficial to a variety of application areas, including question answering and natural language understanding. Previous work explored collecting commonsense knowledge triples automatically from text to increase the coverage of current commonsense knowledge graphs. We investigate a few machine learning approaches to mining commonsense knowledge triples using dictionary term definitions as inputs and provide some initial evaluation of the results. We start from extracting candidate triples using part-of-speech tag patterns from text, and then compare the performance of three existing models for triple scoring. Our experiments show that term definitions contain some valid and novel commonsense knowledge triples for some semantic relations, and also indicate some challenges with using existing triple scoring models.
dc.relation.urihttps://tw.rpi.edu/project/machine-common-sense-mcs-multi-modal-open-world-grounded-learning-and-inference-mowgli
dc.subjectMachine Common Sense (MCS) Multi-modal Open World Grounded Learning and Inference (MOWGLI)
dc.titleCommonsense Knowledge Mining from Term Definitions


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