Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Segmentation-free Compositional $n$-gram Embedding   We propose a new type of representation learning method that models words,
phrases and sentences seamlessly. Our method does not depend on word
segmentation and any human-annotated resources (e.g., word dictionaries), yet
it is very effective for noisy corpora written in unsegmented languages such as
Chinese and Japanese. The main idea of our method is to ignore word boundaries
completely (i.e., segmentation-free), and construct representations for all
character $n$-grams in a raw corpus with embeddings of compositional
sub-$n$-grams. Although the idea is simple, our experiments on various
benchmarks and real-world datasets show the efficacy of our proposal.

12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess