Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning the grammar of drug prescription: recurrent neural network
  grammars for medication information extraction in clinical texts   In this study, we evaluated the RNNG, a neural top-down transition based
parser, for medication information extraction in clinical texts. We evaluated
this model on a French clinical corpus. The task was to extract the name of a
drug (or a drug class), as well as attributes informing its administration:
frequency, dosage, duration, condition and route of administration. We compared
the RNNG model that jointly identifies entities, events and their relations
with separate BiLSTMs models for entities, events and relations as baselines.
We call seq-BiLSTMs the baseline models for relations extraction that takes as
extra-input the output of the BiLSTMs for entities and events. Similarly, we
evaluated seq-RNNG, a hybrid RNNG model that takes as extra-input the output of
the BiLSTMs for entities and events. RNNG outperforms seq-BiLSTM for
identifying complex relations, with on average 88.1 [84.4-91.6] % versus 69.9
[64.0-75.4] F-measure. However, RNNG tends to be weaker than the baseline
BiLSTM on detecting entities, with on average 82.4 [80.8-83.8] versus 84.1
[82.7-85.6] % F- measure. RNNG trained only for detecting relations tends to be
weaker than RNNG with the joint modelling objective, 87.4% [85.8-88.8] versus
88.5% [87.2-89.8]. Seq-RNNG is on par with BiLSTM for entities (84.0
[82.6-85.4] % F-measure) and with RNNG for relations (88.7 [87.4-90.0] %
F-measure). The performance of RNNG on relations can be explained both by the
model architecture, which provides inductive bias to capture the hierarchy in
the targets, and the joint modeling objective which allows the RNNG to learn
richer representations. RNNG is efficient for modeling relations between
entities or/and events in medical texts and its performances are close to those
of a BiLSTM for entity and event detection.

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The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: cond-mat