  Commonsense 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.
