ral
ssBioMed Cent
BMC Bioinformatics
Open Acce
Methodology article
Information extraction from full text scientific articles: Where are
the keywords?
Parantu K Shah
1,2
, Carolina Perez-Iratxeta
1,2
, Peer Bork*
1,2
and
Miguel A Andrade
1,2,3
Address:
1
Biocomputing, European Molecular Biology Laboratory, Heidelberg, Germany,
2
Department of Bioinformatics, Max Delbrück Center
for Molecular Medicine, Berlin-Buch, Germany and
3
Present address: Bioinformatics group, Ottawa Health Research Institute, Ottawa, Canada
Email: Parantu K Shah - shah@embl.de; Carolina Perez-Iratxeta - cperez@embl.de; Peer Bork* - bork@embl.de;
Miguel A Andrade - andrade@embl.de
* Corresponding author
Information extractionfull text articlekeywordgene namedata miningtext mining
Abstract
Background: To date, many of the methods for information extraction of biological information
from scientific articles are restricted to the abstract of the article. However, full text articles in
electronic version, which offer larger sources of data, are currently available. Several questions
arise as to whether the effort of scanning full text articles is worthy, or whether the information
that can be extracted from the different sections of an article can be relevant.
Results: In this work we addressed those questions showing that the keyword content of the
different sections of a standard scientific article (abstract, introduction, methods, results, and
discussion) is very heterogeneous.
Conclusions: Although the abstract contains the best ratio of keywords per total of words, other
sections of the article may be a better source of biologically relevant data.
Background
Most applications of information extraction from the sci-
entific medical bibliography use the Abstract of the publi-
cation (for review see for example [1–3]). In the context of
information extraction in molecular biology it is usually
understood that the information to be extracted from an
article are words regarding biological concepts that could
synthesize the main points of the article (keywords).
Therefore the Abstract of a paper is a good target for infor-
mation extraction because by definition an abstract syn-
thesizes the content of the article. Moreover, abstracts are
available in public databases. However, nowadays most
journals are also available in electronic version, and thus
It is obvious that the full text of an article contains more
information than its Abstract. However, in approaching
full text analysis several problems must be tackled. On the
one hand, the storage of full text articles requires more
disk space and the analysis needs more computational
capacity. On the other hand, an Abstract, as a summary,
contains a high frequency of relevant terms (keywords),
but this may not be the case of the rest of the article.
Other questions regard the quality of the information car-
ried by different sections of an article. First of all, is the
information in full text organized enough so that key-
words can be extracted? Secondly, different biological
Published: 29 May 2003
BMC Bioinformatics 2003, 4:20
Received: 5 March 2003
Accepted: 29 May 2003
This article is available from: http://www.biomedcentral.com/1471-2105/4/20
© 2003 Shah et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all
media for any purpose, provided this notice is preserved along with the article's original URL.Page 1 of 9
(page number not for citation purposes)
full text articles can be used for information extraction. concepts (for example, gene and protein names, tissue
names, organisms, experimental conditions, etc.) may be

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
located in different parts of the article. Or it could be that
a word has a different meaning depending on the section
where it is located (the word has a context dependent
meaning). For example, regarding gene names, those
found in the Methods section may refer mostly to analyt-
ical tools rather than being relevant to the biological phe-
nomenology described in the whole article. In summary,
it would be good to quantify and qualify the information
in a full text article before embarking in large scale extrac-
tion of particular items of information.
With this goal in mind, we analyzed in this work the kind
of information that is attached to different parts of an arti-
cle and we tried to quantify how much information can be
found in each section of an article. This should help to
state some guidelines for researchers attempting to extract
particular keywords (words synthesizing the content of
the article) from full text articles.
Results
Text Corpus
As previously stated, the major objective of this work was
to compare the information defined as keyword content
carried by different sections of a paper, especially the dif-
ferences between the Abstract and the rest. Therefore, as
source for our analysis we used a set of full text articles
with a regular section structure, in our study having a
defined Abstract, Introduction, Methods, Results, and Dis-
cussion (A, I, M, R, D). Another requirement was certain
homogeneity of style across the articles (for example, a
similar length of the Methods section) and, since there is
great interest in the field of data mining on the detection
of gene names, the subject should be related to Genetics.
Thus, we chose the 104 articles published in Nature Genet-
ics from June 1998 (volume 19, issue 2) to June 2001 (vol-
ume 28, issue 2), which comply with the AIMRD
structure. Note that other journals, or even the Letters of
the very same Nature Genetics, might have a different struc-
ture (for example, lacking separated I, M, R, D sections).
Selection of Keywords
To simplify matters, and following our previous work [4],
we focused on the extraction of relevant words (key-
words) regarding objects, detected as nouns from natural
text by a standard grammatical tagger (TreeTagger, Hel-
mut Schmid, IMS, Stuttgart University, http://
www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/).
In order to derive keywords from the section of an article,
we first compute the associations between the words in
the section. Here, we take the sentence as the unit of text
to look for associations, that is, two words are associated
in the context of a section if they co-occur repeatedly in
sentences within that section (see METHODS).
Since words associated strongly to many other words are
relevant to the matter that is dealt in the article [5] we use
a score (K) that is higher for words with many and strong
relations to other words (see METHODS). This measure is
used to select words as keywords, in this case, related to
objects such as proteins, genes, organisms, etc.
In order to evaluate the performance of the keyword
detection, we observed how the selected keywords
matched the MeSH (Medical Subject Headings, http://
www.nlm.nih.gov/mesh/) terms attached by indexers at
the National Library of Medicine to these 104 articles
(18.6 on average). Since MeSH terms can be composed of
several words (for example, "Learning Disorders"), we
selected those composed of a single word (6.80 terms on
average). We noted that the most unspecific (for example,
animal) were often not present in the text and thus could
not be matched by a keyword as opposed to species names
(mouse, mycobacterium, human), or anatomical terms (hip-
pocampus, cerebellum, breast). Of those single-word MeSH
terms, 4.91 were found on average in the article (as
nouns), and 2.22 were among the set of selected keywords
(above K >= 0.3). Obviously, a more accurate comparison
to MeSH terms would require the detection of bigrams,
and trigrams (keywords composed of multiple words),
but this is out the scope of our work. The recall when
matching the original MeSH terms (6.80 on average) went
down from 4.91 / 6.80 = 0.72 in the dictionary of 470.6
different nouns present in an article to 2.22 / 6.80 = 0.33
in the 66.6 keywords selected. However, since the size of
the list of all nouns found in an article (470.6) is much
larger than the number of keywords (66.6), the precision
in matching the MeSH terms of an article increased from
4.91 / 470.6 = 0.010 to 2.22 / 66.6 = 0.033.
Keyword Selection by Section
The number of words selected upon a threshold in the K
value varies for different sections (see Figure 1). The first
observation is that there are a small number of words that
have much better K scores than the rest. This means that
the organization of words makes it possible to extract key-
words for all the five considered sections.
The number of selected words is very similar for all sec-
tions for very high values of K (above 0.8). Above a thresh-
old on K (K >= 0.5; see Table 1) the resulting number of
keywords is quite similar for Introduction and Methods
(around 15 for each) with the other three sections produc-
ing around nine keywords. However, if one accounts for
the size of the sections it is obvious that the frequency of
keywords (selected with K >= 0.5) per noun is the best in
the Abstract (0.18), followed by the Introduction (0.08),
with Methods, Results, and Discussion lagging behind.Page 2 of 9
(page number not for citation purposes)
This justifies data mining strategies that focus in the anal-
ysis of Abstracts in order to minimize computational

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
resources. However, this result already indicates that not
all keywords are in the Abstract, and that therefore mining
the rest of the article may be worthy.
Sections Display Heterogeneous Information
As a way to show that the keyword content in different
sections is heterogeneous, we examined which keywords
(if any) were selected in all the sections of an article. Our
results indicate that, as it could be expected, not many
keywords are present in every section and those are not
very relevant. Even for a low threshold of K >= 0.3, there
is on average only one of such general keywords per arti-
cle. Those are often non-informative words such as
"gene", or "protein". This indicates that the information is
unevenly distributed across the sections of the article, that
is, different sections contain different kind of
information.
We illustrate the heterogeneity of the information by sec-
tion with the keywords selected (for K >= 0.5) for a partic-
ular article [6] (Figure 2). This work deals with a mutation
of the Nf1 gene of mouse (an exon loss) that produces
learning deficits. The only keyword present in every sec-
tion is the organism under study, the mouse. If the Meth-
ods section is excluded, only one single more keyword
(mutation) is selected. Other three-section overlaps give
more interesting keywords such as the name of the gene
under study (Nf1, neurofibromin), a domain contained in
the resulting protein (GAP), the method for testing learn-
ing performance of mice (maze), or the resulting pheno-
contained in each section. For example, the keywords
unique to the Methods section deal with reagents and
techniques (antibody, amersham, tris, primer).
In order to quantify the differences and similarities of con-
tent across the article we have used the number of key-
words that are shared between different sections (Table
2). The values indicate that the Methods section is the
most different of all. In Methods, the content is usually
focused on the techniques and protocols used, and not so
much on the biological phenomena that is the main sub-
ject of the article. This alone explains why those keywords
present in every section (for example protein, gene) are
scarce and uninteresting.
Regarding similarities between sections, A, I, and D are
evenly similar among them, and R is the closest to M, as it
is shown when plotting the distance matrix of Table 2 as a
dendogram (see Figure 3). This is probably due to the fact
that the Results section deals with the protocols used,
although not as explicitly as the Methods section. The Dis-
cussion focuses again on the biological results (stressing
their relation to the current knowledge) without detailing
the techniques that have already been explained in Meth-
ods and justified in Results.
This result indicates that each section contains certain key-
words that are unique to the section. In the following we
try to characterize what are the differences in content
between sections.
Qualitative Analysis of Subjects per Section
To make a deeper analysis of the kind of information
present in each of the sections, we classified in seven cate-
gories a set of words present in our corpus of 104 articles
(among the most frequent nouns). In order to do so as
unambiguously as possible, we selected words that
matched MeSH descriptors also consisting on that single
word and belonging to only one major MeSH category
(see METHODS). We added another category not present
in MeSH, that of "Units, Dimensions, & Parts" in order to
account for many terms that are currently not MeSH terms
but are of interest to us.
Figure 1
Average number of keywords versus K for A, I, M, R, and D
sections. The average number of nouns per section is, A =
52, I = 171, M = 404, R = 600, D = 331.
0.1
1
10
100
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
K
k
e
y
w
o
r
d
s
A
I
M
R
D
Table 1: Keyword selection per section.
all K >= 0.3 K >= 0.4 K >= 0.5
A 52.17 19.44 14.42 9.77
I 171.32 31.03 20.47 14.00
M 404.19 54.24 28.50 15.80
R 599.98 24.74 12.74 7.85
D 331.04 26.16 14.25 8.75
Average number of nouns per section (all), or number of those Page 3 of 9
(page number not for citation purposes)
type (impairment, lethality). Keywords unique to different
sections tend to correspond to the different information
selected as keywords for three different thresholds on the K score.

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
The results (See Figure 4a) indicate that the large sections
are a good source of keywords, obviously Methods
gathering many terms related to techniques. Introduction,
Results and Discussion contain a good deal of informa-
tion regarding diseases. However, again, the Abstract sec-
tion is shown as the best source for most subjects
those typical of the Methods section (Techniques &
Equipment; Chemicals & Drugs).
Distribution of Gene Names
Since the detection of gene and protein names is a very
important subject, broadly used for the detection of mac-
Table 2: Average number of keywords (K >= 0.5) shared by two sections for the corpus of 104 articles.
AIMRD
A 2.01 0.92 1.77 2.20
I 2.01 0.81 1.34 2.02
M 0.92 0.81 1.55 1.02
R 1.77 1.34 1.55 1.99
D 2.20 2.02 1.02 1.99
Table 3: Detection of gene names appearing only in the Methods section.
Ref
Restriction endonucleases
Msp1 v27.n3.277
Incorrect: Pst1 v19.n4.340
Sac1 v27.n4.375
Vector name
Psg5 v23.n3.287
Cell strain
Tig3 v26.n3.291
Definition of a Yeast strain
Can1, Leu2, Lys2, Trp1 v26.n4.415
In array
Faf1 v20.n3.266
Correct (technical context): Growth detection
Mcm5, Mcm6 v25.n3.263
Platelet mRNA analysis
Pbp2 v23.n2.166
Primers used to determine embryo sex
Zfy1, Zfy2 v27.n1.31
Analysis of mutant phenotypes
Pmd1 v24.n4.355
cDNA probe
Rab2 v19.n2.134
Correct: SNP found in cDNA
Add3, Npr2 v22.n3.239
Identifier given
Pom1 v28.n3.223
Detection of meiosis specific genes
Mei4, Mek1, Sps4, Zip1 v26.n4.415
Ref: reference of the article in Nature Genetics by volume, issue number and first page of the article.Page 4 of 9
(page number not for citation purposes)
regarding frequency of keywords (Figure 4b) except for romolecular interactions (see for example [7]), and
because, as stated in the introduction, we are concerned

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
about the relevance of matching gene names in different
sections of an article, we examined the distribution of
gene names across sections.
From a long list of genes names derived from the SWISS-
PROT database [8], we selected a very restricted set of 539
genes whose names are composed of three letters fol-
lowed by one single digit, thus very difficult to be mis-
taken to other words not being genes. For example, there
are gene names called Not or That. Shorter names (e.g. A6)
can also be a problem. A total of 224 gene names out of
the 539 was matched in 76 of the 104 articles. The Results
section was the one with the greatest number of unique
gene names (Figure 5a). Again, the Abstract, and then the
In order to illustrate the problems that affect gene-name
identification if context is ignored (even using gene names
apparently easy to recognize) (discussed for example in
[9]) we checked manually the context of gene names that
were exclusively mentioned in the Methods section. Of
the 224 genes, just 24 were mentioned in the Methods sec-
tion of the corresponding 14 articles and not elsewhere
(see Table 3). In five of the 14 articles, the name was refer-
ring to a non-gene object (three restriction endonucleases,
a vector name, and a fibroblast cell strain). In five articles,
the gene was mentioned in a technical context (usually,
the gene mRNA level was used for analysis of cell state)
and no biological process involving the gene was
described. In only five articles we found the mention of
Figure 2
The keywords selected for an article [6] with a K >= 0.5 are represented as they appear in the different sections of the article.
exon
mouse
memory
neurofibromin
maze
brain
mutation
nf1
impairment
learning
disability
ras
motor
rabbit
antibody
amersham
tris
primer
deficit
developmentdifferentiation predisposition
discrimination
iris
lethality
gtpase
gestation
gap
tumour
AIMRD
AIRD
AID
AIM
Other
M
I
R
D
APage 5 of 9
(page number not for citation purposes)
Introduction, are the sections with the highest frequency
of these names (Figure 5b).
the gene name relevant (See Table 3). Additionally, we
noted that of these 24 gene names, at least two (Pbp2,

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
Pom1) could refer to two non-homologous (unrelated)
genes, and another one (Sac1) to four; such polysemous
gene names complicate gene identification from text.
Biologists are aware of such problems (see for example
[10]). In summary, extreme caution should be applied
with gene names appearing uniquely in the Methods sec-
tion because the context of gene names there is very differ-
ent to that seen in the rest of the article. If automated
methods to extract gene names from text are applied to
the Methods section, those that explore the context of
gene names using part-of-speech tagging (for example,
[11]) or Hidden Markov Models (for example, [7]) should
then perform better than those that just take co-occur-
rences of gene names [12,13].
Discussion
There is a clear need for doing information extraction of
biological data from full text scientific articles and the
means for doing it are there with computers better suited
for faster computation every day and new methodologies
for Natural Language Processing that can be used for bio-
medical literature (see for example [14]). Regarding the
source of data, the full text electronic versions of journals
are now more the rule than the exception, with initiatives
in the way towards the construction of large public repos-
itories of such information (although hotly debated; see
In this work we have shown that the distribution of infor-
mation (as keywords) in full text articles is heterogeneous
and that there is certain correspondence of article sections
with different kind and density of relevant data. The
Abstracts are shown as the best repository from the point
of view of having many keywords in a short space, justify-
ing previous information extraction approaches. The lack
of large repositories of full text articles in contrast to the
current eleven million of references (many of them with
their abstract) in the MEDLINE database, are another
advantage of the Abstract approach.
However, we have shown that there is much more rele-
vant information (at least in a ratio of 1:4 regarding gene
names, anatomical terms, organism names, etc.) in the
rest of the article. We have demonstrated that the informa-
tion is structured enough to get important numbers of
relevant keywords, but that for certain words (such as
gene names) caution has to be taken regarding the context
of the word.
We propose that the text mining of full text articles should
be approached with different strategies for different sec-
tions. Beyond the Abstract, the Introduction looks like the
best place to look for protein and gene names (and inter-
actions) since it is probably describing current knowledge.
The Discussion section, that interprets the results and put
them in context with the current knowledge, looks like the
third best place for mining such information, with Meth-
ods probably as the worst place. The Results section could
be problematic given its mixed nature between Methods
and the rest.
Regarding other subjects, such as keywords about biolog-
ical concepts (species, tissues, diseases, etc.), again the
Abstract and then the Introduction section look like the
best sections to mine regarding frequency of such key-
words, but Results and especially Discussion seem better
from a quantitative point of view. The Methods section is
clearly appropriated for looking for technical data, meas-
urements, and chemicals. Respect to chemicals, again,
their context can be completely different in this section
compared to the rest.
Conclusions
Extraction of biological information from full text looks
promising, but context must be regarded. Part of this con-
text is given by the situation of the text under analysis
within the article. Therefore, tuning the extraction of
information to the section is probably a good strategy,
and for particular tasks some sections should be avoided.
We have shown that the kind of simplistic annotation that
Figure 3
In order to display graphically the similarity between sections
regarding keyword content, we took the inverse of the aver-
age number of shared keywords (Table 2) as a measure of
dissimilarity between sections, and we plotted it as a dendog-
ram (using [19]).
Methods
Results
Discussion
Abstract
IntroductionPage 6 of 9
(page number not for citation purposes)
about PubMed Central [15,16]). constitutes tagging a fragment of an article as belonging to
a characteristic section is already useful for text mining.

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
Figure 4
Word categories present in the five sections under analysis. Classes according to MeSH are A (Anatomy), B (Organisms), C
(Diseases), D (Chemicals & Drugs), E (Techniques & Equipment), G (Biological Sciences). An additional class X was defined in
this work (Units, Dimensions, & Parts). The number of words used for the analysis was 36 (class A), 14 (B), 11 (C), 47 (D), 33
(E), 41 (G), 49 (X). (a) Average number of occurrence of words of each subset per section. (b) Frequency of words of each
Word frequency
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Abstract Intoduction Methods Results Discussion
(a)
(b)
Word retrieval by section
0
2
4
6
8
10
12
Abstract Intoduction Methods Results Discussion
Anatomy
Organisms
Diseases
Chemical & Drugs
Techniques &
Equipment
Biological Sciences
Units, Dimensions, &
PartsPage 7 of 9
(page number not for citation purposes)
subset per total number of words for each section.

BMC Bioinformatics 2003, 4 http://www.biomedcentral.com/1471-2105/4/20
But further tagging using markup codes in XML style [17]
identifying biological objects and concepts (under
development; see for example [18] or the GENIA project
http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/) could ulti-
mately make text mining a children's game. We hope for
future interfaces for writers of Molecular Biology articles
that should do the job upon validation by the authors (for
example, marking every occurrence of a gene name with a
unique and stable link to any of the existing gene
sequence databases). For this to happen, the collabora-
tion between both scientists and publishers will be very
important.
Methods
Derivation of Associations between the words of a section
Given a section from an article, we split the text in sen-
tences using a standard part of speech tagger (TreeTagger).
We only computed associations between the words
identified from the tagging as nouns. Following [4], the
association between two words (w
i
,w
j
) (for example, "cell"
and "cycle") can be modeled as the degree of inclusion of
one word into the other ( ) which can defined as the
fuzzy binary relation given by: ,
that is, the ratio of the number sentences where both
words w
i
and w
j
co-occur to the number of sentences the
they happen in natural text. For example, in some Cell
Biology context, the word "cycle" could appear always
related to the word "cell" (as in "cell cycle"), but the word
"cell" can be related to many other words such as in "cell
growth", "cell membrane", or "cell nucleus". Accordingly,
the inclusion value of the word "cycle" into "cell" will be
close to one and the inclusion value of the word "cell"
into the word "cycle" will be close to zero.
Selection of Keywords
We identify a word as relevant for the text analyzed if it
establishes many and strong relations to other words (fol-
lowing [4]). Therefore, in a given section, we define a
score for a word w
i
that is equal to ,
normalized to the maximum value found for K of any
word in that section. Then, the keywords of the section are
defined as those words that have a K score above a certain
value.
Classification of Words in Subjects
In order to classify words into categories we used the fol-
lowing procedure. We chose the MeSH (Medical Subjects
Headings) classification from the National Library of
Medicine. All MeSH terms (including official synonyms)
composed of one single word were selected and then the
stem of the word was computed using TreeTagger. The
words present in our corpus of 104 articles were ordered
by frequency and all words occurring more than 200 times
were selected. Those matching the selected single-word
MeSH headers from six categories (A, B, C, D, E, and G;
See the caption of Figure 4 for descriptions) were selected
as belonging to those classes. In order to avoid possible
miss-annotations, words matching more than one
category were discarded. Manual analysis of the resulting
table of associations was carried out in order to check the
associations and to make new ones. A new class not
present in MeSH (the X class of "Units, Dimensions, &
Parts") was generated in order to include a large number
of terms mainly present in the Methods section.
Authors' Contributions
PS carried out the analysis of the keyword distribution
from a database of full text articles. CP developed and
applied the method to compute keywords. MA prepared
the figures (except fig 2 by PS) and conceptualised the
structure of the paper. PB and MA co-directed the project
and contributed to the final manuscript. All authors col-
laborated during the whole length of the project. All
authors read and approved the final manuscript.
Acknowledgements
We are grateful to the developers and maintainers of the different data-
Figure 5
Distribution of matches to a set of 224 gene names across
sections. (a) Average number of unique gene names per sec-
tion. (b) Frequency of different gene names per total of
nouns for each section.
(a) (b)
genes
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
A
b
s
t
r
a
c
t
I
n
t
o
d
u
c
t
i
o
n
M
e
t
h
o
d
s
R
e
s
u
l
t
s
D
i
s
c
u
s
s
i
o
n
frequency of genes
0
0.001
0.002
0.003
0.004
0.005
A
b
s
t
r
a
c
t
I
n
t
o
d
u
c
t
i
o
n
M
e
t
h
o
d
s
R
e
s
u
l
t
s
D
i
s
c
u
s
s
i
o
n

I
W
µ

I
ij
ij
i
W
ww
WW
W
,
()
=
∩
Kw
i
I
ji
ij
W
=
()
≠
∑
µ

,Page 8 of 9
(page number not for citation purposes)
word w
i
occurs. This is an asymmetric relation very appro-
priate to model hierarchical relations between words as
bases used in this work (SWISSPROT, MeSH, Nature Genetics full text
repository), to Helmut Schmid (IMS, Stuttgart University) for distributing

Publish with BioMed Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
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Your research papers will be:
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TreeTagger, to Harindar S. Keer for help with the data management, and
to the members of our group at EMBL-Heidelberg for fruitful discussions.
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