Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Taxonomies of Concepts and not Words using Contextualized Word
  Representations: A Position Paper   Taxonomies are semantic hierarchies of concepts. One limitation of current
taxonomy learning systems is that they define concepts as single words. This
position paper argues that contextualized word representations, which recently
achieved state-of-the-art results on many competitive NLP tasks, are a
promising method to address this limitation. We outline a novel approach for
taxonomy learning that (1) defines concepts as synsets, (2) learns
density-based approximations of contextualized word representations, and (3)
can measure similarity and hypernymy among them.

28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph