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An adaptive annotation approach for biomedical entity
and relation recognition
Seid Muhie Yimam . Chris Biemann . Ljiljana Majnaric .
Šefket Šabanović . Andreas Holzinger
Received: 25 November 2015 / Accepted: 25 January 2016 / Published online: 27 February 2016
 The Author(s) 2016. This article is published with open access at
Abstract In this article, we demonstrate the impact of
interactive machine learning: we develop biomedical entity
recognition dataset using a human-into-the-loop approach.
In contrary to classical machine learning, human-in-theloop approaches do not operate on predefined training or
test sets, but assume that human input regarding system
improvement is supplied iteratively. Here, during annotation, a machine learning model is built on previous annotations and used to propose labels for subsequent
annotation. To demonstrate that such interactive and iterative annotation speeds up the development of quality
dataset annotation, we conduct three experiments. In the
first experiment, we carry out an iterative annotation
experimental simulation and show that only a handful of
medical abstracts need to be annotated to produce suggestions that increase annotation speed. In the second
experiment, clinical doctors have conducted a case study in
annotating medical terms documents relevant for their
research. The third experiment explores the annotation of
semantic relations with relation instance learning across
documents. The experiments validate our method qualitatively and quantitatively, and give rise to a more personalized, responsive information extraction technology.
Keywords Interactive annotation  Machine learning 
Knowledge discovery  Data mining  Human in the loop 
Biomedical entity recognition  Relation learning
1 Introduction and motivation
The biomedical domain is increasingly turning into a dataintensive science, and one challenge with regard to the
ever-increasing body of medical literature is not only to
extract meaningful information from this data, but to gain
knowledge, insight, and to make sense of the data [1]. Text
is a very important type of data within the biomedical
domain and in other domains: it is estimated that over 80 %
of electronically available information is encoded in
unstructured text documents [2]. As an example in the
medical domain, patient records contain large amounts of
text which have been entered in a non-standardized format,
consequently posing a lot of challenges to processing of
such data and for the clinical doctor the written text in the
medical findings is still the basis for any decision making
[3, 4]. Further, scientific results are communicated in text
form, consequently for the biomedical domain text is an
indispensable data type for gaining knowledge [5].
Modern automated information extraction (IE) systems
usually are based on machine-learning models, which
require large amount of manually annotated data to specify
the model according to the task at hand. Unfortunately,
S. M. Yimam (&)  C. Biemann
TU Darmstadt CS Department, FG Language Technology,
64289 Darmstadt, Germany
C. Biemann
L. Majnaric  Š. Šabanović
Josip Juraj Strossmayer University of Osijek Faculty of
Medicine Osijek, Osijek, Croatia
Š. Šabanović
A. Holzinger
Research Unit HCI-KDD Institute for Medical Informatics,
Statistics and Documentation Medical University Graz,
Auenbruggerplatz 2, 8036 Graz, Austria
Brain Informatics (2016) 3:157–168
DOI 10.1007/s40708-016-0036-4
particularly in the medical domain, experts have obligations with higher priorities, thus it is very expensive and
cumbersome to annotate a large number of training
examples. In order to alleviate this problem, there is a need
for an approach where human annotators are facilitated to
annotate faster than the traditional way, in order to produce
required annotations in less time.
A further complication, not only but especially in the
medical domain, is the general difficulty with standardization in the light of genericity of the standard and
specificity of the application scenario. While large-scale
taxonomies ontologies exist both for the general [6, 7] and
the medical domain (e.g. UMLS)1., their sheer size impose
a high burden on anyone that tries to make knowledge in
text explicit: annotators would have to learn these ontologies or at least relevant parts of these ontologies in order to
properly carry out their task. Further, as [8] points out,
ontologies are usually created in an author-centric fashion:
without a particular application at hand, authors of
ontologies have to discretize the space of things into concepts. This discretization, however might or might not be
suitable and might or might not yield practical conceptualizations for a particular task or application. While automatically inducing ontologies from text [9] or other
statistical methods to induce conceptualizations and taxonomies have the premise to alleviate the author-centricity
by yielding a resource that neatly fits the domain as defined
by the corpus, they are still hard to tune towards particular
modelling goals of users, which might only find a small
fraction of the textual material relevant for their task.
While we have seen tremendous efforts in the past years
to standardize and link lexical taxonomies and ontologies2,
there has not been a widespread use of such structured
resources for the formal representation of the semantics of
text. We attribute this to their excessive size and their
author-centricity as outlined above, as well as to the lack of
information for being able to assign their concepts to
respective terms in unstructured text: just because e.g. all
viruses are known in a database, it does not follow from
this that it is possible to find their occurrences in text (e.g.
because of ambiguous abbreviations, short forms and
variants, idiosynchracies of the subfield etc.). Here, we
propose a radical break with this traditional way of
knowledge representation: instead, users should be able to
choose their own set of categories per given task or problem, and thus should be able to grow their own local
ontology without the need (but eventually with the possibility) of connecting it to existing upper ontologies, and
users should ground their conceptualization in the respective texts of their current interest.
In this article, we tackle the extractions of entity mentions and their relations from biomedical texts, specifically
from MEDLINE abstracts3, using a recent human-into-theloop automation strategy that has not been applied in the
medical domain before. Unlike named entity recognition
(NER) systems on e.g. the news domain, entity recognition
on medical domains comprises of extractions of technical
terms in the broader medical and biological arena such as
name of diseases, proteins, substances and so on, see e.g.
[10, 11].
Such an automation approach is specifically very
important for the medical domain, as a full manual annotation is extremely expensive. Medical professionals in
turn, however, are willing to perform this task only diligently if it matches their current field of interest. The
human-into-the-loop automation approach enables users to
start the automation process without pre-existing annotations, and works by suggesting annotations as soon as the
users have annotated a rather small number of documents.
This annotate-little and predict-little strategy is deemed
adequate for biomedical domains as it (1) produce quality
annotation in a very short period of time, (2) the approach
is adaptive in such a way that newly evolving concepts or
entities will not be ignored by an old and static prediction
classification model, and 3) the conceptualization (i.e.
entity types and their typed relations) can be chosen and
extended by the user during the annotation process. Thus,
this human-in-the-loop approach follows the principles of
the recently emerging cognitive computing paradigm that
proposes more adaptive, iterative and interactive humanmachine interaction [12, 13].
Note that while models trained on a small number of
entity mentions cannot be expected to produce high-quality
automatic labels, however their annotation suggestions
might still be useful for the task at hand, in turn, help to
produce more annotations in a short time that eventually
improve the quality of the automatic labels.
We conduct three experiments to exemplify and evaluate our human-into-the-loop approach of entity mention
annotation for the medical domain. In the first experiment
(Sect. 5.1), we simulate the interactive machine learning
approach by incrementally processing the BioNLP-NLPBA
2004 named entity annotated data set [14]. During the
simulation, a classifier model is first trained on very few
annotations and we measure the number and quality of
correctly predicted annotations in the next chunk of the
data, which subsequently is added to the training, simulating the annotation process. With this simulation, we can
learn whether annotating very few documents already
produces reasonable and faithful predictions so that it
2 see 3
158 S. M. Yimam et al.
relieves users from annotating every document in the data
In the second experiment (Sect. 5.2), we put our
approach to practice and apply it in a use case where
medical professionals annotate documents in order to
support research on their particular question of interest.
Specifically, the task used for this study is focused towards
the investigations of the causes of the B-chronic lymphocytic leukemia (B-CLL) on MEDLINE abstracts and users
annotate terms with their respective entity classes with socalled span annotations, which means that annotators
assign an entity label to a word or a subsequent set of
words in the text. Here, we compare two setups where
annotators are presented, or not presented with suggestions
from the classifier in the interactive annotation interface.
This experiment sets out to clarify whether medical professionals perceive our human-in-the-loop approach as
appropriate and helpful in quantitative terms and in a
qualitative assessment.
The third experiment (also in Sect. 5.2) extend this
notion further: here, we focus on the relations between such
entities, which is a more interesting type of knowledge
from an application perspective, but also poses more
challenging problem for incremental machine learning.
Here, we let our medical expert annotate e.g. interactions
between proteins or relations between antibodies and
antigenes. We notice that the system quickly picks up on
user-defined relations, and found that our medical expert
had to define additional relations to relations given in a
standard dataset in order to model her requirements.
The main contributions of this article are three-fold:
first, we show how using the human-in-the-loop approach,
we can outperform an approach that relies only on expert
annotation without the human in the loop. Second, we
demonstrate that even with a little amount of annotation, a
good performance for annotation suggestion can be
reached, resulting in a substantial annotation speedup.
Third, we exemplify how the human-in-the-loop approach
in text annotation allows the customization of entities and
relation types for the user’s need. Part of this article was
already presented in a shorter form in [15].
2 Related work
This section gives a brief overview of related work in
adaptive machine learning as well as named entity tagging
and relation learning for the medical domain in general.
2.1 Human into the loop
Automated machine learning algorithms work well in
certain environments. However, biomedical data are full of
probability, uncertainty, incompleteness, vagueness, noise,
etc., which makes the application of automated approaches
difficult, yet often impossible. Moreover, the complexity of
current machine learning algorithms has discouraged
medical professionals from the application of such solutions. There is also the issue of acceptability and provenance, cf. [16]: since their decisions might be life-critical,
medical professionals will not accept automatic systems,
even with high precision, which cannot justify the rationale
for the automatic decision. While there exist rather simple
learning algorithms (e.g. memory-based learning, [17]) that
provide reasonable explanations (e.g. in form of similar
examples or situations), they need more training data to
reach the same performance level as more advanced and
complex algorithms (e.g. deep learning [18]). However,
most complex approaches fail to give interpretable reasons
for their automatic classification, which calls for facilitation of training data creation, especially for sensitive and
life-critical domains; a further drawback with complex
machine learning approaches is that their training is done in
epochs over the whole dataset and there is no straightforward way to add additional labeled examples to the model.
However, for increasing the quality of such approaches,
the integration of the expert’s domain knowledge is
indispensable. The interaction of the domain expert with
the data would greatly enhance the whole knowledge discovery process chain. Interactive machine learning (IML)
puts the human into the loop to enable what neither a
human nor a computer could do on their own, cf. [1]. For
this, only machine learning algorithms are suitable that
support online learning. In this work, we will use a perceptron-based online learning algorithm to generate suggestions for manual text annotation in the medical domain.
These annotations are subsequently used to generate better
suggestions, as the model continuously updates based on
human interaction with our annotation tool.
2.2 Interactive and adaptive learning
Static machine learning assumes that the actual state of the
‘‘domain universe’’ can be sufficiently acquired by listing
all available data sets at particular time. In the contrast,
adaptive machine learning assumes the possibility that
there might exist unrecorded facts at particular time, which
can only be appear at some point in the future. This,
however, is rather the standard situation than the exception:
think e.g. of a recommendation system for an online
shopping platform: if it was static, there would be no
recommendations for any product that was launched after
the system was set up. Authors of [19] address an industrial
case study (tile manufacturing process) and found out that
the classical machine learning setup faced difficulties such
as (1) feedback is usually obtained after a process is
An adaptive annotation approach for biomedical 159
completed, which might help the system, (2) some variables can change through time, and (3) error correction is
always done after observation. The research by [20] on
clustering a large number of documents using an interactive recommender system shows that users can sort documents into clusters significantly faster with an interactive
recommender system than correcting the output of a static
automated method. On top of simple user feedback in [21],
such as accepting and rejecting suggestions, complex
feedback like choosing the best features, suggestions for
the re-weighting of features, proposing new features and
combining features remarkably improve the system.
Moreover, experiments in [22] examine the effect of
allowing end users to do feature labeling, instead of
annotating instances of training data: especially for small
amounts of training, the feature labeling approach was
shown to be effective. In our work, we do not incorporate
feature labeling, but we will consider it in our future work.
2.3 NER for medical domains
Recent years have seen a surge on biomedical text processing (see [23] for a survey), most of which rely on the
GENIA corpus [24], which is a collection of biomedical
abstracts. It is mainly annotated for linguistic structures
such POS tagging and syntax annotation, semantic annotation of entities and so on [25, 26]. The work of [27]
focuses on the automatic detections of multiple biomedical
entities using a single-word classification approach in
contrast to earlier works in the area focusing on single
entity types such as proteins or genes. In this approach,
they use features such as word attributes and contextual
information. To alleviate the bottleneck of manual named
entity annotation for medical texts, [28] have set up a
crowdsourcing project on Amazon Mechanical Turk
( to annotate three entity types. The
research shows that using crowdsourcing is a viable alternative to annotate medical texts at scale for entity types
that are understood by laymen like ‘‘medication’’. However, for a more complex and fine-grained distinction that
requires domain knowledge, medical professionals are
2.4 Relation learning in the medical domain
EDGAR [29] is a natural language processing system that
extracts information about drugs and genes relevant to
cancer from the biomedical literature. The entities that
EDGAR focuses on are genes, cells and drugs extracted
from the MEDLINE abstracts. The system uses a statistical
part-of-speech tagger for word class recognition and subsequently uses semantic and pragmatic information to
construct possible relations. The entity relations (REL)
task, a supporting task of the BioNLP shared task 2011
[30], deals with the extraction of two types of part-of
relations between a gene or protein and an associated
entity. The task focused on two specific types of objectcomponent relations, that holding between a gene or protein and its part (domain, regions, promoters, amino acids,
etc.) and that between a protein and a complex that it is a
subunit of, namely protein-component and subunit-complex. The highest performing system achieves an F-score of
57.7 %. The work of [31] addresses the problem of automatic extractions of protein interactions from bioscience
texts. Using graphical models and a neural network, it was
possible to achieve a comparably high accuracy (64%) in
extracting relations from biosmedical text. For training, a
domain specific database of the HIV-1 human protein
interaction database containing two types of interactions,
protein interactions, and human gene knock-downs (replication interactions) was employed.
While described works constitute the state of the art of
biomedical relation extraction, their level of performance is
not sufficient for automatic processing. In our approach, we
add the human in the loop to the equation, ensuring high
accuracy on the specific relations of interest to our human
3 Methodology
3.1 Annotation learning
The development of large amounts of high quality training
data at one shot is hard and even undesirable [32]. Instead,
an interactive machine learning methodology is more
applicable where the machine-learning model is enhanced
not using the prevailing train-learn-evaluate technique, but
improving the model in a more iterative fashion.
Interactive learning focuses on enhancing an existing
machine-learning model based on newly acquired information, which is not possible in a classical machine
learning setting. The benefit of interactive learning is
many-fold, such as (1) the classifier model gets better and
better as new training examples are added to the training
data, (2) when there is a sudden change to the underlying
data set, what is known as concept drift, the machinelearning model gets updated accordingly [33], and (3) it
largely reduces the total annotation time required to
annotate the whole dataset. Most importantly, such
approach will (4) not require a pre-existing annotation
dataset so that it is truly responsive and incremental, fully
adaptive to the user’s need, and it makes such approach
more affordable when integrated into a larger IE system.
While it is possible to use pre-existing sets of labels for
entities and their relations in interactive learning, this
160 S. M. Yimam et al.
incremental methodology (5) also allows to define and
extend these label sets at any point in time during the
annotation. This might be an especially effective feature
for avoiding the mismatch between the ontology or taxonomy of labels and the text collection.
As the machine-learning model can be enriched incrementally, applications employing this model will not be
affected, as the system can still draw suggestions from the
old model while building the new model. This approach
overcomes the limitations where systems have to wait until
full training and prediction cycles are completed,
decreasing deployment time.
3.2 The WebAnno annotation tool
To conduct our study, we have slightly extended WebAnno
[34], which is a general purpose web-based annotation tool
for a wide range of linguistic annotations. WebAnno allows
to freely configure different span and relation annotations
and is widely used for the creation of linguistic datasets in
the natural language processing community as wenn as in
the digital humanities.
WebAnno features an in-build automation mechanism
as described in [35]. In this so-called ‘‘automation mode’’,
users can see automatic suggestions made by the system,
where they can either accept or ignore. WebAnno [34]
features a split-pane visualization, where annotation is
performed in the upper pane by selecting text and choosing
a label. In the lower pane, suggestions are displayed, which
can be accepted and appear as annotations in the upper
pane upon clicking on them, cf. Fig. 3.
We have extended WebAnno for this study to not only
suggests span annotations but also relations between spans.
While the span suggestion mechanism is very generic and
relies on perceptron-based online learning of sequence
tagging [36], the relation suggestion is restricted to the
scenario where both spans are suggested and the relation
between the terms of the two spans has been annotated
before in the same or a previously annotated document.
This restriction on relation learning produces highly precise suggestions, but fails to propose yet unseen relations.
3.3 Medical NER tagging and relation extraction
Medical named entity mention recognition is a well-researched area with a large number of datasets used in
competitions [14, 37–40]. These mainly focus on entity or
mention and chunk detections and relation extraction.
Unfortunately, biomedical annotation tasks are still challenging unlike other language processing tasks due to the
fact that most of the annotations require highly experienced
professional annotators, as discussed above.
To demonstrate the effect of interactive learning on
biomedical entity tagging, we used thee BioNLP-NLPBA
2004 corpus and train a classifier using a rather generic
sequence tagging system developed for German named
entity recognition [41] based on CRF suite [42]. The system is highly configurable regarding features and data
formats. For this study, we use basic standard features to
characterize the text: Character and word features, which
consists of the first and last character n grams (n=3) of the
current token as affixes, considered in a time-shifted window of two tokens around the word token in focus. We also
incorporated automatically induced part-of-speech (POS)
tag clusters as features, which are based on the system by
[43] trained on a medline 2004 dataset. For unseen tokens
in the cluster, the pretree multi-purpose word classifier tool
from the ASV toolbox [44] is used to approximate the
unsupervised POS tags, which are induced following the
principles of structure discovery [45]. Furthermore, word
shape features that reflect capitalization and character
classes (e.g. numbers vs. letters), were found to be relevant
for biomedical mentions, as the shape of such entities often
differs from non-entity tokens.
4 Annotation problem use case
4.1 Entity annotation
In this section, the use case of our medical research professionals is laid out. It focuses on understanding the
interplay between risk factors and genetic presuppositions
with a leukemia cancer.
B-chronic lymphocytic leukemia (B-CLL), a malignant
hematopoetic neoplasm of B-lymphocytes (B cells), is the
most common leukemia in the westernized world [46]. Yet,
its risk factors and underlying mechanisms are still
unknown. Some features of this malignancy, such as the
incidence increasing with age and low proliferative
capacity combined with impaired apoptosis (homeostatic
cell death), categorize this disorder more as a chronic aging
disease, than as a ‘‘real’’ leukemia, known to arise from the
primary genetic defect and the subsequent block in immune
cell differentiation [47]. On the other hand, accumulated
evidence indicate that the pathogenesis of some commonly
occurring cancers, such as breast, or colon cancer, as well
as of some types of lymphomas (malignant neoplasms of
the lymphoid tissue), can be explained by the complex
interplay of age-related and lifestyle-related mechanisms,
operating mainly through chronic inflammation and
impaired insulin dependent metabolism, known as insulin
resistance condition (decreased insulin action in target
tissues followed by chronic hyperglycemia) [48–50].
An adaptive annotation approach for biomedical 161
Biological links towards cancerogenesis and lymphomagenesis go via impaired cell homeostasis mechanisms, including apoptosis and proliferation, as well as
inter-cellular and intra-cellular signaling [51, 52]. Medical
expert posed a hypothesis that the same risk factors and
mechanisms stay also in the background of the pathogenesis of B-CLL. Exact evidence in the literature is absent.
Literature search and reasoning could be demanding,
because of the need to revealing many complex relationships between the numerous sets of entities and the syntagmatic constructs.
In order to alleviate the efforts of meaningful literature
searching, we used the tool of adaptive annotation learning.
Firstly, the medical expert prepared a set of selected
abstracts, downloaded from the medline. Then, based on a
limited number of specific medical entities, including cell,
condition, disorder, gene, molecule, protein, molecular
pathway and substance, she annotated the important structures throughout the entire text body and made them visible.
4.2 Entity automation and relation copy annotator
In this second setup, we took datasets from the BioNLP
2011 shared task [40] (entity relations supporting task
(REL)). Our tasks include (a) train a classifier for entity
annotation, (b) correct suggestions provided by the classifier and when appropriate add new annotation to the
dataset, and (c) create a relation annotation between the
existing entity annotations. In addition to the relation types
specified in the BioNLP shared task, our medical expert
annotated additional relation types since the existing ones
were not deemed sufficient for her research question.
Table 1 shows the relation types specified at the shared task
and our newly added relation types.
Already at this point, we can conclude that an adaptive
approach to relation extraction is more adequate to the
scenario of biomedical annotation and knowledge management: Only through an adaptive approach where users
can freely addd new types of entities and relations it is
possible to tune the explicified information towards the
user’s needs: while the general-purpose setting in the
BioNLP 2011 task has provided some useful relation types,
it did not cover some of the relations of interest and a static
approach would have left the user no choice but to disregard these or leave them in unstructured form.
For rapid relation annotation, we have incorporated a
relation copy annotator into WebAnno where relation suggestions are provided (at the lower pane in Fig. 1) as soon as
annotators create relation annotations (in the upper pane in
Fig. 1). This functionality has the following advantages:
(a) more occurrences of the same relation are automatically
suggested for the remaining parts of the document and for
subsequent documents, and (b) an annotator can easily copy
suggestions to the annotation view if the suggestions provided are correct. The impact of the relation copy annotation
will be explained in the following section.
5 Experiments and evaluation
5.1 Simulating interactive learning
In order to prove that interactive machine learning can
yield a quality-annotated data set in a short training loop,
Table 1 Relation types from (a) the BioNLP shared task 2011 and (b) identified during the relation annotation process by our medical expert
(a) Relation types from BioNLP 2011
Equivalent Two protein or cell components are equivalent
The protein-component is a less specific object-component relation that holds between a gene or protein and its component,
such as a protein domain or the promoter of a gene.
Subunit-complex is a component-object relation that holds between a protein complex and its subunits, individual proteins
(b) New relation types
Two proteins linked with the same reaction; the first one is responsible for starting the reaction and the second one
responsible for its sustainability
An immune protein with the ability to specifically bound the antigen, a foreign substance, and to neutralise its toxicity
Cell-marker A set of surface proteins typical for a cell lineage or a stage of development
Cell-variant The main cell lineage and the subtypes which are the parts of this larger cell family
DNA-transcript DNA and its mRNA (messenger RNA) which translate the gene‘s message to a protein product
Two proteins or molecules which can bind to each other because oft he complementarity of the binding site
Protein-variant Two proteins with the similar structure and function
162 S. M. Yimam et al.
we conduct our first experiment based on the BioNLPNLPBA 2004 data set. The data set is divided into an
increasing size of documents simulating interactive annotation. As it can be seen from Table 2 and Fig. 2, a (simulated) annotation of only 40 sentences already predicted
an adequate amount of suggestions where users can quickly
accept or modify and proceed to the next iteration. Aiming
at maximizing F-score as the harmonic mean of precision
and recall, we can clearly observe in Table 2 that, after
simulated annotating of about 500 sentences, the gain in
performance decreases, which implies that only annotating
small portion of the sentences produces reasonable suggestions that are mostly acceptable by the annotator. Also,
we can see that more annotations beyond 5000–10000
sentences are subject to diminishing returns, i.e. it takes an
increasing number of annotations to achieve the same
amount of relative improvements, the more annotations are
used for training. In a human-in-the-loop setting, this can
be detected during the process, and could be a sign for
requiring more advanced features in the machine learning
setup. This confirms our findings described in [53], where
we have reached a speedup of factor 3 already with moderately accurate annotation suggestions.
5.2 Automation and relation copy annotator
Using the BioNLP 2011 shared task dataset, we have
conducted experiments constituting two phases, i.e. entity
automation and correction as well as relation annotation
and suggestion.
5.2.1 Entity automation
We have randomly selected 20 documents from the given
training dataset (from a total of 780 documents) and train
the in-built classifier of WebAnno (cf. Sect. 5.2). These
documents contain 312 entity annotations and our classifier
produced 687 annotation suggestions. Later we have presented the suggestions to our medical expert to re-annotate
the documents using the suggestion. Our annotator produces a total of 752 entity annotations, which contains in
addition to the protein and Entity annotations, a third type
of entity called cell. Table 3 shows the performance of our
automation system and expert annotator against the 20
documents (with gold annotations) form the BioNLP2011
REL shared task dataset.
5.2.2 Relation copy annotator
Once the entity annotation is completed, we have conducted relation annotation with the help of WebAnno copy
annotator. The copy annotator produces relation suggestions in the same document where the source and target
entity annotations as well as the covered texts match. The
gold dataset contains 102 relation annotations while our
annotator produces 397 relation annotations. Table 4 shows
the average number of relation suggestions per document
and across all documents.
We note that we are able to attain F-scores comparable
to the state of the art, which validates out approach in
comparison to previous approaches. More importantly, we
expect a significant increase in performance when the
system is used productively and can continuously extend
its capabilities in long-running deployments.
Fig. 1 Relation copy annotator: upper pane: relation annotation by the annotator. Lower pane: relation suggestions that can be copied by the user
to the upper pane
Table 2 Evaluation result for the BioNLP-NLPBA 2004 task using
an interactive online learning approach with different sizes of training
dataset (in number of sentences) measured in precision, recall and
F-measure on the fixed development dataset
Sentence Recall Precision F-score
40 27.27 39.05 32.11
120 37.74 44.01 40.63
280 46.68 51.39 48.92
600 53.23 54.89 54.05
1240 57.83 57.74 57.78
2520 59.35 61.26 60.29
5080 62.32 64.03 63.16
10,200 66.43 67.50 66.96
18,555 69.48 69.16 69.32
An adaptive annotation approach for biomedical 163
5.3 Qualitative Assessment
In addition to the quantitative experimental simulation
done in Sect. 5.1, we have conducted practical annotation
and automation experiments using a total of 10 MEDLINE
abstracts that were chosen in the context of our use case
described in Sect. 4, using WebAnno as described in Sect.
5.2. The experiment was conducted in two rounds. In the
first round, medical experts have annotated 5 abstracts
comprising a total of 86 sentences for specific medical
entities as described in Sect. 4. Once the first round of
annotations was completed, the automation was started
using WebAnno’s automation component in order to provide initial suggestions. As displayed in Fig. 3, the
automation component already suggests some entity
annotations immediately after the first round. Using the
automation suggestions, the expert continued annotating.
After another 9 annotated abstracts that serve as training
for the sequence tagging model, the quality and quantity of
suggestions have again increased, see Fig. 3.
Qualitatively, annotators found that using the automation component, they perceived a significant increase in
annotation speed. This confirms results in [53], where
adaptive annotation automation in WebAnno can speed up
the annotation process by a factor of 3 to 4 in comparison
to a traditional annotation interface without suggestions.
On a further note, the WebAnno tool was perceived as
adequate and useable by our medical professionals,
requiring only very limited usage instructions.
5.4 Analysis of the automation and relation copy
As it can be seen from Table 3, on one hand, the machine
learning automation produces better performance on the
general entity annotation types than our expert annotator.
This indicates that the entities annotated in this dataset are
very coarse level which should be re-annotated, specifically
designed to meet domain and task requirements. On the
other hand, our expert annotator outperforms the automation system on protein annotation types. This is because
protein annotations are more specific and unambiguous to
The relation copy annotator behaves as expected, as
shown in Table 4, where it is possible to produce more
Fig. 2 Learning curve showing
the performance of interactive
automation for BioNLPNLPBA 2004 data set using
different sizes of training data.
(Color figure online)
Table 3 Machine learning automation and expert annotator performance for BioNLP 2011 REL shared task dataset
Mode Annotator type Recall Precsion F-score
Entity 61.94 49.31 54.91
Protein 57.31 50.97 53.95
Entity 29.11 22.90 25.63
Protein 71.94 59.28 65.00
Table 4 Analysis of relation suggestions. For a total of 20 randomly
selected BioNLP2011 REL shared task documents, there has been a
total of 397 relations annotated. In the process, the system produces
on average 2.1 suggestions per relations and 19.85 suggestions per
document. The last column shows an average number of relation
suggestions across several documents
Docs All Rels Perrel Perdoc Acrossdocs
20 397 193 2.1 19.85 0.18
164 S. M. Yimam et al.
similar relation suggestion on the same document than
across several documents. We can learn from this process
that (1) the low number of relation suggestion across several documents (randomly selected from the dataset) indicates that we should employ human experts in the selection
of documents which fit the domain of interest so that our
system behaves as expected, and (2) a simple relation copy
annotator fails to meet the need of producing adequate
relation suggestions hence a proper machine learning
algorithm for relation suggestion should be designed.
6 Conclusion and future outlook
In this work, we investigated the impact of adaptive
machine learning for the annotation of quality training
data. Specifically, we tackled medical entity recognition
and relation annotation on texts from MEDLINE, the largest collection of medical literature on the web. Identifying
the need of entity tagging for applications such as IE,
document summarization, fact exploring and relation
extraction, and identifying the annotation acquisition
Fig. 3 Automation suggestions using the WebAnno automation component after annotating 5 (b) initial response 9 (c) additional abstracts.
Correct suggestions are marked in grey, while wrong suggestions are marked in red. a is the correct annotation by a medical expert. (Color
figure online)
An adaptive annotation approach for biomedical 165
bottleneck which is especially severe in the medical
domain, we have carried out three experiments that show
the utility of a human-in-the-loop approach for suggesting
annotations in order to speed up the process and thus to
widen this bottleneck. In the first experimental setup, we
have used an existing BioNLP-NLPBA 2004 data set and
run experimental simulation by incrementally processing
the dataset to simulate the human in the loop. Using a
generic sequence tagger, we showed that annotating very
few sentences already produces enough correct predictions
to be useful, suggesting that interactive annotation is a
worthwhile enterprise from the beginning of an annotation
project. In the second setup, we have engaged medical
professionals in the annotation of medical entities in documents that were deemed relevant for the investigation of
the cause of malignant B-CLL. The freely available
WebAnno annotation tool ( has been
used for the annotation and automation process and annotators found that the adaptive annotation approach (1)
makes it fast and easy to annotate medical entities, and (2)
useful entity suggestions were already obtained after the
annotation of only five medline abstracts, and suggestions
subsequently improved tremendously after having annotated another nine abstracts, reducing the annotation effort.
The third experiment extends the same notion to relation
annotation, resulting in a graph of entities and their relations per document, which gives rise to a more formalized
notion of medical knowledge representation and personal
knowledge management.
On a larger perspective, our results demonstrate that a
paradigm change in machine learning is feasible and
viable. Whereas the mantra of the past has been ’there is no
(annotated) data like more (annotated) data’ for supervised
machine learning, suggesting large annotation efforts
involving many human annotators, it becomes clear from
our experiments that these efforts can be sped up tremendously by switching to an approach where the human can
continuously improve the model by annotation while using
the model to extract information, with the especially good
news that the largest model improvements are achieved
already very early in the process, as long as the domain is
While such an adaptive approach to machine learning
that factors in the user into the equation still calls for new
evaluation methodologies to be assessed in all its aspects, it
is deemed more adequate, more immediate and quicker
deployable. It also fits better the shift towards an interactive, more natural, more adaptive, more contextualized and
iterative approach under the umbrella of cognitive
Acknowledgments The development of WebAnno and the research
on adaptive machine learning was supported by the German Federal
Ministry of Education and Research (BMBF) as part of the CLARIND infrastructure and by German Research Foundation (DFG) as part
of the SEMSCH project.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://crea, which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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Seid Muhie Yimam is currently a doctoral student at LT lab, TU
Darmstadt, Germany, under the supervision of Chris Biemann. He has
been working as a scientific software engineer at LT and UKP labs
since September 2012. He has been involved in the development of
NLP tools such as WebAnno and Network of the Day and also been
assisted by Chris Biemann in teaching and student supervision. His
main research interests are in the integration of adaptive machine
learning approaches into an interactive annotation tools and semantic
writing aids. He worked as a semantic web software developer at
Okkam srl, a start-up semantic web company in Trento, Italy, from
September 2011 to August 2012. He received an advanced master
degree in Human Language Technology and Interfaces from the
An adaptive annotation approach for biomedical 167
University of Trento, Italy in September 2011. He also received his
MSc and BSc degrees in Computer Science from the Department of
Computer Science, Addis Ababa University, Ethiopia in July 2009
and July 2004, respectively.
Chris Biemann is the Head of Language Technology at the
Computer Science Department of TU Darmstadt, Germany. His
research interests are in natural processing, in particular aspects of
statistical semantics, unsupervised learning, and cognitive computing.
He obtained his doctorate in Computer Science from the University of
Leipzig in 2007, and his dissertation was published in 2011 in an
extended version as a Springer monograph. After his dissertation, he
worked in an industry as a PostDoc fellow at semantic search startup
Powerset and the web search engine Microsoft Bing. In 2011, he got
appointed as an Assistant Professor of Language Technology. He has
published over 100 papers on natural language processing, computational linguistics, text visualization, and psychology. Recently, he
was awarded an Academic Fellowship by Sweden’s Wallenberg
Foundation at the University of Gothenburg.
Ljiljana Majnaric recently got appointed as an Associate Professor
of Family Medicine and Internal Medicine, Life Science, Faculty of
Medicine, University of Osijek, Croatia. She is a specialist in Family
Medicine and the Head of the Department of Family Medicine. She
completed her master degree in Internal Medicine, Life Science, and
her doctoral degree in Public Health, both licensed by the Faculty of
Medicine, University of Zagreb. She also completed her postdoctoral
study in Clinical Immunology and Allergology, Faculty of Medicine,
University of Zagreb. She has participated in several research projects
of the Ministry of Science, Education and Sports, Croatia, and was a
leader of the Project Bioinformatics in Clinical Medicine, funded by
the University of Osijek, Croatia. She has published 37 reviewed
papers, 9 c/c, 8 sci/sci exp. She has a wide scope of research interests,
including Primary Health Care, clinical and integrative medicine,
aging diseases, vaccination, clinical immunology, and knowledge
discovery from databases. She has been a member of the Holzinger‘s
HCI-KDD International Network since 2010.
Šefket Šabanović is currently a doctoral student in Life Science at the
Faculty of Medicine, University of Osijek, Croatia, and he accepted a
mentorship for his doctoral work from Ljiljana. He is a specialist in
Family Medicine and Emergency Medicine and a Director of the
Health Centre of Županja, a town in the Eastern Slavonia Region. He
has published 4 papers as a co-author, and his doctoral work on using
electronic health records for research purposes is at the final stage of
Andreas Holzinger is the Head of the Holzinger Group, HCI-KDD,
Institute of Medical Informatics, Statistics and Documentation at the
Medical University Graz, and an Associate Professor of Applied
Computer Science at the Institute of Information Systems and
Computer Media at Graz University of Technology. His research
interests are in machine learning to solve problems in health
informatics. He obtained his PhD in Cognitive Science from Graz
University in 1998 and his Habilitation (second PhD) in Computer
Science from TU Graz in 2003. He was a Visiting Professor in Berlin,
Innsbruck, Vienna, London (2 times) and Aachen, and he founded the
Expert Network HCI-KDD to foster a concerted international effort to
support human intelligence with machine intelligence. He is an
Associate Editor of Knowledge and Information Systems (KAIS) and
a member of IFIP WG 12.9 Computational Intelligence.
168 S. M. Yimam et al.

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