Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NSEEN: Neural Semantic Embedding for Entity Normalization   Much of human knowledge is encoded in text, available in scientific
publications, books, and the web. Given the rapid growth of these resources, we
need automated methods to extract such knowledge into machine-processable
structures, such as knowledge graphs. An important task in this process is
entity normalization, which consists of mapping noisy entity mentions in text
to canonical entities in well-known reference sets. However, entity
normalization is a challenging problem; there often are many textual forms for
a canonical entity that may not be captured in the reference set, and entities
mentioned in text may include many syntactic variations, or errors. The problem
is particularly acute in scientific domains, such as biology. To address this
problem, we have developed a general, scalable solution based on a deep Siamese
neural network model to embed the semantic information about the entities, as
well as their syntactic variations. We use these embeddings for fast mapping of
new entities to large reference sets, and empirically show the effectiveness of
our framework in challenging bio-entity normalization datasets.

25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio