Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Dynamic Author Representations with Temporal Language Models   Language models are at the heart of numerous works, notably in the text
mining and information retrieval communities. These statistical models aim at
extracting word distributions, from simple unigram models to recurrent
approaches with latent variables that capture subtle dependencies in texts.
However, those models are learned from word sequences only, and authors'
identities, as well as publication dates, are seldom considered. We propose a
neural model, based on recurrent language modeling, which aims at capturing
language diffusion tendencies in author communities through time. By
conditioning language models with author and temporal vector states, we are
able to leverage the latent dependencies between the text contexts. This allows
us to beat several temporal and non-temporal language baselines on two
real-world corpora, and to learn meaningful author representations that vary
through time.

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The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph