Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Multi-Relational Semantics Using Neural-Embedding Models   In this paper we present a unified framework for modeling multi-relational
representations, scoring, and learning, and conduct an empirical study of
several recent multi-relational embedding models under the framework. We
investigate the different choices of relation operators based on linear and
bilinear transformations, and also the effects of entity representations by
incorporating unsupervised vectors pre-trained on extra textual resources. Our
results show several interesting findings, enabling the design of a simple
embedding model that achieves the new state-of-the-art performance on a popular
knowledge base completion task evaluated on Freebase.

11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org