Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Author2Vec: A Framework for Generating User Embedding   Online forums and social media platforms provide noisy but valuable data
every day. In this paper, we propose a novel end-to-end neural network-based
user embedding system, Author2Vec. The model incorporates sentence
representations generated by BERT (Bidirectional Encoder Representations from
Transformers) with a novel unsupervised pre-training objective, authorship
classification, to produce better user embedding that encodes useful
user-intrinsic properties. This user embedding system was pre-trained on post
data of 10k Reddit users and was analyzed and evaluated on two user
classification benchmarks: depression detection and personality classification,
in which the model proved to outperform traditional count-based and
prediction-based methods. We substantiate that Author2Vec successfully encoded
useful user attributes and the generated user embedding performs well in
downstream classification tasks without further finetuning.

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The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
23
The categories of this paper are: solv-int