Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey on Recent Advances in Sequence Labeling from Deep Learning
  Models   Sequence labeling (SL) is a fundamental research problem encompassing a
variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition
(NER), text chunking, etc. Though prevalent and effective in many downstream
applications (e.g., information retrieval, question answering, and knowledge
graph embedding), conventional sequence labeling approaches heavily rely on
hand-crafted or language-specific features. Recently, deep learning has been
employed for sequence labeling tasks due to its powerful capability in
automatically learning complex features of instances and effectively yielding
the stat-of-the-art performances. In this paper, we aim to present a
comprehensive review of existing deep learning-based sequence labeling models,
which consists of three related tasks, e.g., part-of-speech tagging, named
entity recognition, and text chunking. Then, we systematically present the
existing approaches base on a scientific taxonomy, as well as the widely-used
experimental datasets and popularly-adopted evaluation metrics in the SL
domain. Furthermore, we also present an in-depth analysis of different SL
models on the factors that may affect the performance and future directions in
the SL domain.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph