Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Verb Sense Clustering using Contextualized Word Representations for
  Semantic Frame Induction   Contextualized word representations have proven useful for various natural
language processing tasks. However, it remains unclear to what extent these
representations can cover hand-coded semantic information such as semantic
frames, which specify the semantic role of the arguments associated with a
predicate. In this paper, we focus on verbs that evoke different frames
depending on the context, and we investigate how well contextualized word
representations can recognize the difference of frames that the same verb
evokes. We also explore which types of representation are suitable for semantic
frame induction. In our experiments, we compare seven different contextualized
word representations for two English frame-semantic resources, FrameNet and
PropBank. We demonstrate that several contextualized word representations,
especially BERT and its variants, are considerably informative for semantic
frame induction. Furthermore, we examine the extent to which the contextualized
representation of a verb can estimate the number of frames that the verb can
evoke.

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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con