Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved
  Phrase Graphs
Zhuoyi Peng Yi Yang
We study the patent phrase similarity inference task, which measures the
semantic similarity between two patent phrases. As patent documents employ
legal and highly technical language, existing semantic textual similarity
methods that use localized contextual information do not perform satisfactorily
in inferring patent phrase similarity. To address this, we introduce a
graph-augmented approach to amplify the global contextual information of the
patent phrases. For each patent phrase, we construct a phrase graph that links
to its focal patents and a list of patents that are either cited by or cite
these focal patents. The augmented phrase embedding is then derived from
combining its localized contextual embedding with its global embedding within
the phrase graph. We further propose a self-supervised learning objective that
capitalizes on the retrieved topology to refine both the contextualized
embedding and the graph parameters in an end-to-end manner. Experimental
results from a unique patent phrase similarity dataset demonstrate that our
approach significantly enhances the representation of patent phrases, resulting
in marked improvements in similarity inference in a self-supervised fashion.
Substantial improvements are also observed in the supervised setting,
underscoring the potential benefits of leveraging retrieved phrase graph
augmentation.