Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Entity Extraction and Assertion Detection for Clinical Text   Negative medical findings are prevalent in clinical reports, yet
discriminating them from positive findings remains a challenging task for
information extraction. Most of the existing systems treat this task as a
pipeline of two separate tasks, i.e., named entity recognition (NER) and
rule-based negation detection. We consider this as a multi-task problem and
present a novel end-to-end neural model to jointly extract entities and
negations. We extend a standard hierarchical encoder-decoder NER model and
first adopt a shared encoder followed by separate decoders for the two tasks.
This architecture performs considerably better than the previous rule-based and
machine learning-based systems. To overcome the problem of increased parameter
size especially for low-resource settings, we propose the Conditional Softmax
Shared Decoder architecture which achieves state-of-art results for NER and
negation detection on the 2010 i2b2/VA challenge dataset and a proprietary
de-identified clinical dataset.

19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th