Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks Ensemble for Detecting Medication Mentions in
  Tweets   Objective: After years of research, Twitter posts are now recognized as an
important source of patient-generated data, providing unique insights into
population health. A fundamental step to incorporating Twitter data in
pharmacoepidemiological research is to automatically recognize medication
mentions in tweets. Given that lexical searches for medication names may fail
due to misspellings or ambiguity with common words, we propose a more advanced
method to recognize them. Methods: We present Kusuri, an Ensemble Learning
classifier, able to identify tweets mentioning drug products and dietary
supplements. Kusuri ("medication" in Japanese) is composed of two modules.
First, four different classifiers (lexicon-based, spelling-variant-based,
pattern-based and one based on a weakly-trained neural network) are applied in
parallel to discover tweets potentially containing medication names. Second, an
ensemble of deep neural networks encoding morphological, semantical and
long-range dependencies of important words in the tweets discovered is used to
make the final decision. Results: On a balanced (50-50) corpus of 15,005
tweets, Kusuri demonstrated performances close to human annotators with 93.7%
F1-score, the best score achieved thus far on this corpus. On a corpus made of
all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26%
mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior
drug extraction system that compares running on such an extremely unbalanced
dataset. Conclusion: The system identifies tweets mentioning drug names with
performance high enough to ensure its usefulness and ready to be integrated in
larger natural language processing systems.

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The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex