diff --git a/paper/references.bib b/paper/references.bib
index 2a212ae7360dcc6e0f2ec89f89109dde3f487b69..00fb534aac09560bf582d54361365dcf49424082 100644
--- a/paper/references.bib
+++ b/paper/references.bib
@@ -25,17 +25,8 @@
file = {Fulltext:/Users/mario/Zotero/storage/IS5LGCET/Bahdanau et al. - 2014 - Neural machine translation by jointly learning to .pdf:application/pdf;Snapshot:/Users/mario/Zotero/storage/GR2XHEZN/1409.html:text/html}
}
-@article{cho_learning_2014,
- title = {Learning phrase representations using {RNN} encoder-decoder for statistical machine translation},
- journal = {arXiv preprint arXiv:1406.1078},
- author = {Cho, Kyunghyun and Van Merriënboer, Bart and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua},
- year = {2014},
- file = {Fulltext:/Users/mario/Zotero/storage/R4LHSJ6G/Cho et al. - 2014 - Learning phrase representations using RNN encoder-.pdf:application/pdf;Snapshot:/Users/mario/Zotero/storage/SGEDKP9H/1406.html:text/html}
-}
-
@incollection{bengio_scheduled_2015,
title = {Scheduled {Sampling} for {Sequence} {Prediction} with {Recurrent} {Neural} {Networks}},
- url = {http://papers.nips.cc/paper/5956-scheduled-sampling-for-sequence-prediction-with-recurrent-neural-networks.pdf},
urldate = {2018-05-18},
booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 28},
publisher = {Curran Associates, Inc.},
@@ -110,4 +101,35 @@
author = {Raffel, Colin and Ellis, Daniel PW},
year = {2015},
file = {Fulltext:/Users/mario/Zotero/storage/V3UB65AD/Raffel und Ellis - 2015 - Feed-forward networks with attention can solve som.pdf:application/pdf;Snapshot:/Users/mario/Zotero/storage/66LDNKRG/1512.html:text/html}
+}
+
+@inproceedings{suominen_overview_2018,
+ series = {Lecture {Notes} in {Computer} {Science} ({LNCS})},
+ title = {Overview of the {CLEF} {eHealth} {Evaluation} {Lab} 2018},
+ booktitle = {{CLEF} 2018 - 8th {Conference} and {Labs} of the {Evaluation} {Forum}},
+ publisher = {Springer},
+ author = {Suominen, Hanna and Kelly, Liadh and Goeuriot, Lorraine and Kanoulas, Evangelos and Azzopardi, Leif and Spijker, Rene and Li, Dan and Névéol, Aurélie and Ramadier, Lionel and Robert, Aude and Zuccon, Guido and Palotti, Joao},
+ year = {2018}
+}
+
+@inproceedings{neveol_clef_2018,
+ title = {{CLEF} {eHealth} 2018 {Multilingual} {Information} {Extraction} task {Overview}: {ICD}10 {Coding} of {Death} {Certificates} in {French}, {Hungarian} and {Italian}},
+ booktitle = {{CLEF} 2018 {Evaluation} {Labs} and {Workshop}: {Online} {Working} {Notes}},
+ publisher = {CEUR-WS},
+ author = {Névéol, Aurélie and Robert, Aude and Grippo, F and Lavergne, Thomas and Morgand, C and Orsi, C and Pelikán, L and Ramadier, Lionel and Rey, Grégoire and Zweigenbaum, Pierre},
+ year = {2018}
+}
+
+@inproceedings{cho_learning_2014,
+ address = {Doha, Qatar},
+ title = {Learning {Phrase} {Representations} using {RNN} {Encoder}–{Decoder} for {Statistical} {Machine} {Translation}},
+ url = {http://www.aclweb.org/anthology/D14-1179},
+ urldate = {2018-05-23},
+ booktitle = {Proceedings of the 2014 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
+ publisher = {Association for Computational Linguistics},
+ author = {Cho, Kyunghyun and van Merrienboer, Bart and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua},
+ month = oct,
+ year = {2014},
+ pages = {1724--1734},
+ file = {Full Text PDF:/Users/mario/Zotero/storage/4NE9THT8/Cho et al. - 2014 - Learning Phrase Representations using RNN Encoder–.pdf:application/pdf}
}
\ No newline at end of file
diff --git a/paper/wbi-eclef18.tex b/paper/wbi-eclef18.tex
index 7a1a508d7b5370072185bfadc7b61468f2433e0b..e9ac4421cfe73447d7d86cf88a6c9e8a94a54fa5 100644
--- a/paper/wbi-eclef18.tex
+++ b/paper/wbi-eclef18.tex
@@ -56,28 +56,27 @@ symptom names. Our model achieves \ldots
\end{abstract}
-\section{Introduction}
-\nm{TODO: Insert text!}
+\section{Introduction}
Automatic extraction, classification and analysis of biological and medical
concepts from unstructured texts, such as scientific publications or electronic
health documents, is a highly important task to support many applications in
research, daily clinical routine and policy-making. Computer-aided approaches
can improve decision making and support clinical processes, for example, by
giving a more sophisticated overview about a research area, providing detailed
-information about the aetiopathology of a patient or disease patterns.
+information about the aetiopathology of a patient or disease patterns.
The CLEF eHealth lab attends to this circumstance through organization of
various shared tasks which aid and support the development of approaches to
-exploit electronically available medical content. In particular, Task 1 of the
-lab was concerned with the extraction and classification of causes of death from
-death certificates originating from different languages. Participants were asked
-to classify the death causes mentioned in the certificates according to the
-International Classification of Disease version 10 (ICD-10). The task has been
-carried out the last two years of the lab, however was only concerned
-with French and English certificates. In contrast, the organizers provided
-annotated death reports as well as ICD-10 dictionaries for French, Italian and
-Hungarian this year. The development of language-independent, multilingual approaches
-was encouraged.
+exploit electronically available medical content \cite{suominen_overview_2018}.
+In particular, Task 1 of the lab was concerned with the extraction and
+classification of causes of death from death certificates originating from
+different languages \cite{neveol_clef_2018}. Participants were asked to classify
+the death causes mentioned in the certificates according to the International
+Classification of Disease version 10 (ICD-10). The task has been carried out the
+last two years of the lab, however was only concerned with French and English
+certificates. In contrast, the organizers provided annotated death reports as
+well as ICD-10 dictionaries for French, Italian and Hungarian this year. The
+development of language-independent, multilingual approaches was encouraged.
\section{Related work}
\nm{TODO: Insert text!}
@@ -177,7 +176,7 @@ represented using the concatenation of the FastText embeddings of all three
languages.}
\label{fig:encoder_decoder}
\end{figure}
-
+
\section{Experiments and Results}
\nj{TODO: Insert text!}