Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A bag-of-concepts model improves relation extraction in a narrow
  knowledge domain with limited data   This paper focuses on a traditional relation extraction task in the context
of limited annotated data and a narrow knowledge domain. We explore this task
with a clinical corpus consisting of 200 breast cancer follow-up treatment
letters in which 16 distinct types of relations are annotated. We experiment
with an approach to extracting typed relations called window-bounded
co-occurrence (WBC), which uses an adjustable context window around entity
mentions of a relevant type, and compare its performance with a more typical
intra-sentential co-occurrence baseline. We further introduce a new
bag-of-concepts (BoC) approach to feature engineering based on the
state-of-the-art word embeddings and word synonyms. We demonstrate the
competitiveness of BoC by comparing with methods of higher complexity, and
explore its effectiveness on this small dataset.

11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph