Conference PaperPDF Available
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1–7,
Berlin, Germany, August 7-12, 2016. c
2016 Association for Computational Linguistics
Results of the 4th edition of BioASQ Challenge
Anastasia Krithara1,Anastasios Nentidis1,George Paliouras1, and Ioannis Kakadiaris2
1National Center for Scientific Research “Demokritos”, Athens, Greece
2University of Houston, Texas, USA
Abstract
The goal of this task is to push the re-
search frontier towards hybrid information
systems. We aim to promote systems and
approaches that are able to deal with the
whole diversity of the Web, especially for,
but not restricted to, the context of bio-
medicine. This goal is pursued by the
organization of challenges. The fourth
challenge, as the previous challenges, con-
sisted of two tasks: semantic indexing and
question answering. 16 systems partic-
ipated by 7 different participating teams
for the semantic indexing task. The ques-
tion answering task was tackled by 37 dif-
ferent systems, developed by 11 different
teams. 25 of the systems participated in
the phase A of the task, while 12 par-
ticipated in phase B. 3 of the teams par-
ticipated in both phases of the question
answering task. Overall, as in previous
years, the best systems were able to out-
perform the strong baselines. This sug-
gests that advances over the state of the art
were achieved through the BIOAS Q chal-
lenge but also that the benchmark in it-
self is very challenging. In this paper, we
present the data used during the challenge
as well as the technologies which were at
the core of the participants’ frameworks.
1 Introduction
The aim of this paper is twofold. First, we aim
to give an overview of the data issued during the
BioASQ challenge in 2016. In addition, we aim to
present the systems that participated in the chal-
lenge and for which we received system descrip-
tions, as well as evaluate their performance. To
achieve these goals, we begin by giving a brief
overview of the tasks, including the timing of the
different tasks and the challenge data. Thereafter,
we give an overview of the systems which par-
ticipated in the challenge and provided us with
an overview of the technologies they relied upon.
Detailed descriptions of some of the systems are
given in lab proceedings. The evaluation of the
systems, which was carried out by using state-of-
the-art measures or manual assessment, is the last
focal point of this paper. The conclusion sums up
the results of this challenge.
2 Overview of the Tasks
The challenge comprised two tasks: (1) a large-
scale semantic indexing task (Task 4a) and (2) a
question answering task (Task 4b).
Large-scale semantic indexing. In Task 4a the
goal is to classify documents from the PubMed1
digital library into concepts of the MeSH2hierar-
chy. Here, new PubMed articles that are not yet
annotated are collected on a weekly basis. These
articles are used as test sets for the evaluation of
the participating systems. As soon as the anno-
tations are available from the PubMed curators,
the performance of each system is calculated by
using standard information retrieval measures as
well as hierarchical ones. The winners of each
batch were decided based on their performance in
the Micro F-measure (MiF) from the family of flat
measures (Tsoumakas et al., 2010), and the Low-
est Common Ancestor F-measure (LCA-F) from
the family of hierarchical measures (Kosmopou-
los et al., 2013). For completeness several other
flat and hierarchical measures were reported (Ba-
likas et al., 2013). In order to provide an on-line
and large-scale scenario, the task was divided into
three independent batches. In each batch 5 test
1http://www.ncbi.nlm.nih.gov/pubmed/
2http://www.ncbi.nlm.nih.gov/mesh/
1
sets of biomedical articles were released consecu-
tively. Each of these test sets were released in a
weekly basis and the participants had 21 hours to
provide their answers. Figure 1 gives an overview
of the time plan of Task 4a.
Biomedical semantic QA. The goal of task 4b
was to provide a large-scale question answering
challenge where the systems should be able to
cope with all the stages of a question answer-
ing task, including the retrieval of relevant con-
cepts and articles, as well as the provision of
natural-language answers. Task 4b comprised
two phases: In phase A, BI OASQ released ques-
tions in English from benchmark datasets created
by a group of biomedical experts. There were
four types of questions: “yes/no” questions, “fac-
toid” questions,“list” questions and “summary”
questions (Balikas et al., 2013). Participants
had to respond with relevant concepts (from spe-
cific terminologies and ontologies), relevant arti-
cles (PubMed articles), relevant snippets extracted
from the relevant articles and relevant RDF triples
(from specific ontologies). In phase B, the re-
leased questions contained the correct answers for
the required elements (articles and snippets) of
the first phase. The participants had to answer
with exact answers as well as with paragraph-sized
summaries in natural language (dubbed ideal an-
swers).
The task was split into five independent batches.
The two phases for each batch were run with a
time gap of 24 hours. For each phase, the partic-
ipants had 24 hours to submit their answers. We
used well-known measures such as mean preci-
sion, mean recall, mean F-measure, mean average
precision (MAP) and geometric MAP (GMAP)
to evaluate the performance of the participants
in Phase A. The winners were selected based on
MAP. The evaluation in phase B for the ideal an-
swers was carried out manually by biomedical ex-
perts on the answers provided by the systems. For
the sake of completeness, ROUGE (Lin, 2004) is
also reported. For the exact answers, we used ac-
curacy for the yes/no questions, mean reciprocal
rank (MRR) for the factoids and mean F-measure
for the list questions.
3 Overview of Participants
3.1 Task 4a
In this subsection we describe the proposed
systems which have sent a description and stress
their key characteristics.
In (Papagiannopoulou et al., 2016) flat classifi-
cation processes were employed for the semantic
indexing task. In particular, they used as a training
set the last 1million articles and kept the last 50
thousand as a validation set. Pre-processing of
the articles was carried out by concatenated the
abstract and the title. One-grams and bi-grams
were used as features, removing stop-words and
features with less than five occurrences in the
corpus. The tf-idf representation has been used
for the features. The proposed system includes
several multi-label classifiers (MLC) that are
combined in ensembles. In particular, they used
the Meta-Labeler, a set of Binary Relevance
(BR) models with Linear SVMs and a Labeled
LDA variant, Prior LDA. All the above models
were combined in an ensemble, using the MULE
framework, a statistical significance multi-label
ensemble that performs classifier selection.
The approach proposed by (Segura-Bedmar et
al., 2016) is based on Elastic Search. They use
ElasticSearch in order to index the training set
provided by the BioASQ. Then, each document
in the test set is translated into a query, that is
fired against the index built from the training set,
returning the most relevant documents and their
MeSH categories. Finally, each MeSH category
is ranked using a scoring system based on the
frequency of the category and the similarity of
relevant documents, which contain the category,
with the test document to classify.
Baselines. During the challenge three systems
were served as baseline systems. The first base-
line is a state-of-the-art method called Medical
Text Indexer (MTI) (Mork et al., 2014) which is
developed by the National Library of Medicine3
and serves as a classification system for articles of
MEDLINE. MTI is used by curators in order to
assist them in the annotation process. The second
baseline is an extension of the system MTI with
the approaches of the first BioASQ challenge’s
winner (Tsoumakas et al., 2013). The third one,
dubbed BioASQ Filtering (Zavorin et al., 2016) is
3http://ii.nlm.nih.gov/MTI/index.shtml
2
February 08
February 15
February 22
February 29
March 07
March 14
March 21
March 28
April 04
April 11
April 18
April 25
May 02
May 09
May 16
1st batch 3rd batch
2nd batch End of Task4a
Figure 1: The time plan of Task 4a.
March 09
March 10
March 23
March 24
April 06
April 07
April 20
April 21
May 4
May 5
2nd batch 4th batch 5th batch3rd batch
1st batch
Phase A
Phase B
Figure 2: The time plan of Task 4b. The two phases for each batch run in consecutive days.
a new extension of the MTI system. In particular,
Learning to Rank methodology is used as a boost-
ing component of the MTI system. The improved
system shows significant gains in both precision
and recall for some specific classes of MeSH head-
ings.
3.2 Task 4b
As mentioned above, the second task of the
challenge is split into two phases. In the first
phase, where the goal is to annotate questions
with relevant concepts, documents, snippets and
RDF triples 9 teams with 25 systems participated.
In the second phase, where teams are requested
to submit exact and paragraph-sized answers for
the questions, 5 teams with 12 different systems
participated.
The system presented in (Papagiannopoulou et
al., 2016) is based on Indri search engine, and
they use MetaMap and LingPipe to detect the
biomedical concepts in local ontology files. For
the relevant snippets, they calculate the semantic
similarity between each one of the sentences
and the query (expanded with synonyms) using a
semantic similarity measure. Concerning phase B,
They provided exact answers only for the factoid
questions. Their system is based on their previous
participation in BioASQ challenge (Papanikolaou
et al., 2014). The system tries to extract the
lexical answer type by manipulating the words
of the question. Then, the relevant snippets of
the question which are provided as inputs for
this tasks are processed with the 2013 release of
MetaMap in order to extract candidate answers.
This year, they have extended their approach by
expanding both the scoring mechanism, as well as
the set of candidate answers.
The system presented in (Yang et al., 2016),
extends the system in (Yang et al., 2015). In
particular, they used TmTool (CH et al., 2016),
in addition to MetaMap, to identify possible
biomedical named entities, especially out-of-
vocabulary concepts. In addition, they also
extract frequent multi-word terms from relevant
snippets to further improve the recall of concept
and candidate answer text extraction. They also
introduced a unified classification interface for
judging the relevance of each retrieved concept,
document, and snippet, which can combine the
relevant scores evidenced by various sources. A
supervised learning method is used to rerank the
answer candidates for factoid and list questions
based on the relation between each candidate
answer and other candidate answers.
The system presented in (Schulze et al., 2016)
relies on the Hana Database for text processing.
It uses the Stanford CoreNLP package for tok-
enizing the questions. Each of the tokens is then
sent to the BioPortal and to the Hana database
for concept retrieval. The concepts retrieved from
3
the two stores are finally merged to a single list
that is used to retrieve relevant text passages
from the documents at hand. The second system
relies on existing NLP functionality in the IMDB.
They have extended it with new functions tailored
specifically to QA.
The approach presented in (gu Lee et al., 2016)
participated in phase A of task 4b. The main
focus was the retrieval of relevant documents and
snippets. The proposed system uses a clusterbased
language model. Then, it reranks the retrieved
top-n sentences using five independent similarity
models based on shallow semantic analysis.
4 Results
4.1 Task 4a
During the evaluation phase of the Task 4a, the
participants submitted their results on a weekly ba-
sis to the online evaluation platform of the chal-
lenge4. The evaluation period was divided into
three batches containing 5 test sets each. 7 teams
were participated in the task with a total of 16
systems. For measuring the classification perfor-
mance of the systems several evaluation measures
were used both flat and hierarchical ones (Balikas
et al., 2013). The micro F-measure (MiF) and the
Lowest Common Ancestor F-measure (LCA-F)
were used to asses the systems and choose the win-
ners for each batch (Kosmopoulos et al., 2013).
12,208,342 articles with 27,301 labels (19.4GB)
were provided as training data to the participants.
Table 1 shows the number of articles in each test
set of each batch of the challenge.
Table 2 presents the correspondence of the sys-
tems for which a description was available and the
submitted systems in Task 4a. The systems MTI
First Line Index, Default MTI, BioASQ Filtering
were the baseline systems used throughout the
challenge. Systems that participated in less than
4 test sets in each batch are not reported in the
results5.
According to (Demsar, 2006) the appropriate way
to compare multiple classification systems over
multiple datasets is based on their average rank
across all the datasets. On each dataset the system
with the best performance gets rank 1.0, the
4http://participants-area.bioasq.org/
5According to the rules of BioASQ, each system had to
participate in at least 4 test sets of a batch in order to be eli-
gible for the prizes.
second best rank 2.0 and so on. In case that two
or more systems tie, they all receive the average
rank.
Tables 3 presents the average rank (according to
MiF and LCA-F) of each system over all the test
sets for the corresponding batches. Note, that the
average ranks are calculated for the 4 best results
of each system in the batch according to the rules
of the challenge6. The best ranked system is
highlighted with bold typeface.
Table 4: Statistics on the training and test datasets
of Task 4b. All the numbers for the documents,
snippets, concepts and triples refer to averages.
Batch Size # of documents # of snippets
training 1307 13.00 17.86
1 100 4.56 6.41
2 100 5.25 6.98
3 100 4.79 6.46
4 100 4.90 7.25
5 97 3.93 6.10
total 1804 10.71 14.77
4.2 Task 4b
Phase A. Table 4 presents the statistics of the
training and test data provided to the participants.
The evaluation included five test batches. For the
phase A of Task 4b the systems were allowed
to submit responses to any of the correspond-
ing types of annotations, that is documents, con-
cepts, snippets and RDF triples. For each of the
categories we rank the systems according to the
Mean Average Precision (MAP) measure (Balikas
et al., 2013). The final ranking for each batch is
calculated as the average of the individual rank-
ings in the different categories. In tables 6 and 7
some indicative results from batch 1 are presented.
The detailed results for Task 4b phase A can
be found in http://participants-area.
bioasq.org/results/4b/phaseA/.
Phase B. In the phase B of Task 4b the systems
were asked to report exact and ideal answers. The
systems were ranked according to the manual
evaluation of ideal answers by the BioASQ
experts (Balikas et al., 2013), and according to
automatic measures for the exact answers.
Table 7 shows the results for the exact answers
for the first batch of task 4a. In case that systems
6http://participants-area.bioasq.org/
general_information/Task4a/
4
Table 1: Statistics on the test datasets of Task 4a.
Batch Articles Annotated Articles Labels per article
1 3,740 569 11.25
2,872 714 12.01
2,599 275 11.09
3,294 520 13.72
3,210 418 11.23
Subtotal 15,715 2,496 11.96
2 3,212 443 10.57
3,213 371 11.37
2,831 534 11.78
3,111 541 10.67
2,470 268 9.82
Subtotal 14,837 2,157 10.94
3 2,994 89 12.08
3,044 353 11.79
3,351 241 10.81
2,630 93 9.77
3,130 50 12.56
Subtotal 15,149 826 11.35
Total 45,701 5,479 11.42
Table 2: Correspondence of reference and submitted systems for Task 4a.
Reference Systems
(Papagiannopoulou et al., 2016) Auth1, Auth2
(Segura-Bedmar et al., 2016) LABDA ElasticSearch, LargeElasticLABDA, LABDA baseline
Baselines ((Mork et al., 2013),(Zavorin et al., 2016)) MTI First Line Index, Default MTI, BioASQ Filtering
Table 3: Average ranks for each system across the batches of the task 4a for the measures MiF and
LCA-F. A hyphenation symbol (-) is used whenever the system participated in less than 4 times in the
batch.
System Batch 1 Batch 2 Batch 3
MiF LCA-F MiF LCA-F MiF LCA-F
iria-1 - - 9.0 9.0 - -
LABDA ElasticSearch - - - - - -
d33p - - - - - -
auth1 2.75 3.25 3.75 3.75 - -
Default MTI 4.0 3.0 5.0 4.5 - -
auth2 - - 6.0 6.25 - -
MeSHLabeler 1.25 1.25 1.25 1.25 - -
LargeElasticLABDA - - - - - -
LABDA baseline - - - - - -
BioASQ Filtering 4.5 4.75 5.75 5.5 - -
MeSHLabeler-2 - - 2.0 2.0 - -
MeSHLabeler-1 1.75 1.75 - - - -
MeSHLabeler-3 - - 3.5 3.25 - -
CSX-1 - - - - - -
MTI First Line Index 5.5 5.75 5.75 6.25 - -
UCSDLogReg - - - - - -
didn’t provide exact answers for a particular
kind of questions we used the symbol “-”. The
results of the other batches are available at
http://participants-area.bioasq.
org/results/4b/phaseB/. From those
results we can see that the systems are achieving
a very high (>90% accuracy) performance in the
yes/no questions. The performance in factoid and
list questions is not as good indicating that there
is room for improvements.
5 Conclusion
In this paper, an overview of the fourth BioASQ
challenge is presented. As the previous chal-
lenges, the challenge consisted of two tasks: se-
mantic indexing and question answering. Over-
all, as in previous years, the best systems were
able to outperform the strong baselines provided
by the organizers. This suggests that advances
over the state of the art were achieved through the
BIOA SQ challenge but also that the benchmark in
5
Table 5: Results for batch 1 for documents in phase A of Task 4b.
System Mean Mean Mean MAP GMAP
Precision Recall F-measure
testtext 0.169 0.5331 0.2276 0.0981 0.0128
ustb prir2 0.158 0.5277 0.2164 0.0973 0.0119
ustb prir4 0.165 0.5254 0.2224 0.0967 0.0109
fdu2 0.147 0.5011 0.2012 0.0885 0.0087
ustb prir3 0.156 0.497 0.2114 0.0869 0.0095
fdu 0.153 0.5086 0.2081 0.0866 0.0095
ustb prir1 0.155 0.4936 0.2097 0.0865 0.0088
fdu4 0.15 0.5057 0.205 0.0859 0.012
fdu3 0.154 0.5184 0.2112 0.0849 0.0109
fdu5 0.149 0.4971 0.2036 0.0823 0.01
KNU-SG Team Korea 0.084 0.2258 0.1065 0.0486 0.0008
HPI-S1 0.1209 0.3266 0.1547 0.0474 0.0012
Auth001 0.069 0.1983 0.0914 0.0375 0.0004
WS4A 0.01 0.0134 0.011 0.0038 0
HPI-S2 0.005 0.0062 0.0054 0.0028 0
Table 6: Results for batch 1 for snippets in phase A of Task 4b.
System Mean Mean Mean MAP GMAP
Precision Recall F-measure
HPI-S1 0.0822 0.1706 0.0917 0.0481 0.0005
KNU-SG Team Korea 0.0482 0.0952 0.0534 0.0266 0.0002
ustb prir2 0.0469 0.1135 0.0503 0.0216 0.0002
ustb prir3 0.0452 0.1070 0.0482 0.0212 0.0002
ustb prir1 0.0409 0.1080 0.0491 0.0211 0.0002
ustb prir4 0.0449 0.1108 0.0477 0.0201 0.0002
testtext 0.0433 0.1098 0.0460 0.0188 0.0002
Table 7: Results for batch 3 for exact answers in phase B of Task 4b.
System Yes/no Factoid List
Accuracy Strict Acc. Lenient Acc. MRR Precision Recall F-measure
fa1 0.9600 0.1154 0.1923 0.1442 0.2500 0.3000 0.2641
Lab Zhu ,Fdan Univer 0.9600 0.1923 0.2692 0.2192 0.1450 0.5929 0.2181
LabZhu,FDU 0.9600 0.1923 0.2692 0.2192 0.1444 0.6214 0.2176
LabZhu FDU 0.9600 0.1923 0.2692 0.2192 0.1420 0.5929 0.2132
Lab Zhu,Fudan Univer 0.9600 0.1923 0.2692 0.2192 0.1455 0.5770 0.2185
oaqa-3b-3 0.5200 0.2308 0.2692 0.2436 0.5396 0.5008 0.4828
WS4A 0.2400 0.0385 0.0385 0.0385 0.1172 0.2817 0.1609
LabZhu-FDU 0.0400 0.1923 0.2692 0.2192 0.1420 0.5929 0.2132
6
itself is very challenging. Consequently, we regard
the outcome of the challenge as a success towards
pushing the research on bio-medical information
systems a step further. In future editions of the
challenge, we aim to provide even more bench-
mark data derived from a community-driven ac-
quisition process.
Acknowledgments
The fourth edition of BioASQ is supported by
a conference grant from the NIH/NLM (number
1R13LM012214-01) and sponsored by the Atypon
company.
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7
... In this experiment, we present a systematic evaluation on biomedical questions provided by the BioASQ challenge so as to compare with BioASQ participant systems. As we previously noted, the BioASQ challenges in phase B (i.e., exact an ideal answers) of Task b provide the test set of biomedical questions along with their golden documents, golden snippets, and questions types [61,62,56] and participant systems [29,31,30,28,32] were asked to answer with exact answers and ideal answers using the golden documents, golden snippets, and golden questions types. For each question, each participating system may return an ideal answer, i.e., a paragraph-sized summary of relevant information. ...
Article
Background and objective Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions. Methods This paper describes the system architecture and an evaluation of the developed end-to-end biomedical QA system named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. It takes natural language questions as input, and outputs both short precise answers and summaries as results. The SemBioNLQA system, dealing with four types of questions, is based on (1) handcrafted lexico-syntactic patterns and a machine learning algorithm for question classification, (2) PubMed search engine and UMLS similarity for document retrieval, (3) the BM25 model, stemmed words and UMLS concepts for passage retrieval, and (4) UMLS metathesaurus, BioPortal synonyms, sentiment analysis and term frequency metric for answer extraction. Results and conclusion Compared with the current state-of-the-art biomedical QA systems, SemBioNLQA, a fully automated system, has the potential to deal with a large amount of question and answer types. SemBioNLQA retrieves quickly users’ information needs by returning exact answers (e.g., “yes”, “no”, a biomedical entity name, etc.) and ideal answers (i.e., paragraph-sized summaries of relevant information) for yes/no, factoid and list questions, whereas it provides only the ideal answers for summary questions. Moreover, experimental evaluations performed on biomedical questions and answers provided by the BioASQ challenge especially in 2015, 2016 and 2017 (as part of our participation), show that SemBioNLQA achieves good performances compared with the most current state-of-the-art systems and allows a practical and competitive alternative to help information seekers find exact and ideal answers to their biomedical questions. The SemBioNLQA source code is publicly available at https://github.com/sarrouti/sembionlqa.
... An exception is the framework proposed by NCBI (Mao et al., 2014), which directly computes the cosine similarities between the questions and the sentences. Another team (Yang et al., 2016) introduced a unified classification interface for judging the relevance of each retrieved concept, document and snippet, which can combine the relevant scores evidenced by various sources (Krithara et al., 2016). ...
Article
Motivation: With the abundant medical resources, especially literature available online, it is possible for people to understand their own health status and relevant problems autonomously. However, how to obtain the most appropriate answer from the increasingly large-scale database, remains a great challenge. Here, we present a biomedical question answering framework and implement a system, Health Assistant, to enable the search process. Methods: In Health Assistant, a search engine is firstly designed to rank biomedical documents based on contents. Then various query processing and search techniques are utilized to find the relevant documents. Afterwards, the titles and abstracts of top-N documents are extracted to generate candidate snippets. Finally, our own designed query processing and retrieval approaches for short text are applied to locate the relevant snippets to answer the questions. Results: Our system is evaluated on the BioASQ benchmark datasets, and experimental results demonstrate the effectiveness and robustness of our system, compared to BioASQ participant systems and some state-of-the-art methods on both document retrieval and snippet retrieval tasks. Availability and implementation: A demo of our system is available at https://github.com/jinzanxia/biomedical-QA.
... In this paper, we investigate the effectiveness of BioBERT in biomedical question answering and report our results from the 7th BioASQ Challenge [7,10,11,21]. Biomedical question answering has its own unique challenges. First, the size of datasets is often very small (e.g., few thousands of samples in BioASQ) as the creation of biomedical question answering datasets is very expensive. ...
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... BioNLP-ST has organized various biomedical IE tasks, usually focused on a specific biological system such as seed development [24], epigenetics and post-translational modifications [80], and cancer genetics [81]. Other community challenges relevant to biomedical text mining include JNLPBA [82], BioASQ [83], i2b2 [84], and ShARe/CLEF eHealth [85]. ...
... Additionally, the CNN is trained with different pre-trained word embedding models and compared with the random initialization. First, the different word embedding models using the toolkit Word2vec (Mikolov, Sutskever, Chen, Corrado and Dean, 2013) are trained on the BioASQ 2016 dataset (Krithara et al., 2016), which contains more than 12 million MedLine abstracts. Skip-gram and continuous bag-of-words (CBOW) architectures of Word2vec are applied with the default parameters used in the C version of the Word2vec toolkit (i.e. ...
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The main hypothesis of this PhD dissertation is that novel Deep Learning algorithms can outperform classical Machine Learning methods for the task of Information Extraction in the Biomedical Domain. Contrary to classical systems, Deep Learning models can learn the representation of the data automatically without an expert domain knowledge and avoid the tedious and time-consuming task of defining relevant features. A Drug-Drug Interaction (DDI), which is an essential subset of Adverse Drug Reaction (ADR), represents the alterations in the effects of drugs that were taken simultaneously. The early recognition of interacting drugs is a vital process that prevents serious health problems that can cause death in the worst cases. Health-care professionals and researchers in this domain find the task of discovering information about these incidents very challenging due to the vast number of pharmacovigilance documents. For this reason, several shared tasks and datasets have been developed in order to solve this issue with automated annotation systems with the capability to extract this information. In the present document, the DDI corpus, which is an annotated dataset of DDIs, is used with Deep Learning architectures without any external information for the tasks of Name Entity Recognition and Relation Extraction in order to validate the hypothesis. Furthermore, some other datasets are tested to evidence the performance of these systems. To sum up, the results suggest that the most common Deep Learning methods like Convolutional Neural Networks and Recurrent Neural Networks overcome the traditional algorithms concluding that Deep Learning is a real alternative for a specific and complex scenario like the Information Extraction in the Biomedical domain. As a final goal, a complete architecture that covers the two tasks is developed to structure the named entities and their relationships from raw pharmacological texts.
... Here, we will briefly introduce other participants' methods of document retrieval employed in the 2016 [37] and 2017 BioASQ [38] challenge. Papagiannopoulou et al. [39] built their system on Indri search engine and a variety of libraries had been used, such as the StAX Parser, the Stanford Parser and the GSON library. ...
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