Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised, Efficient and Semantic Expertise Retrieval   We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.

34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas