Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Relation Representations for Universal Schemas   Traditional relation extraction predicts relations within some fixed and
finite target schema. Machine learning approaches to this task require either
manual annotation or, in the case of distant supervision, existing structured
sources of the same schema. The need for existing datasets can be avoided by
using a universal schema: the union of all involved schemas (surface form
predicates as in OpenIE, and relations in the schemas of pre-existing
databases). This schema has an almost unlimited set of relations (due to
surface forms), and supports integration with existing structured data (through
the relation types of existing databases). To populate a database of such
schema we present a family of matrix factorization models that predict affinity
between database tuples and relations. We show that this achieves substantially
higher accuracy than the traditional classification approach. More importantly,
by operating simultaneously on relations observed in text and in pre-existing
structured DBs such as Freebase, we are able to reason about unstructured and
structured data in mutually-supporting ways. By doing so our approach
outperforms state-of-the-art distant supervision systems.

32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat