Zone Identification in Biology Articles as a Basis for Information Extraction 
Yoko MIZUTA and Nigel COLLIER 
National Institute of Informatics 
2-1-2 Hitotsubashi, Chiyoda-ku,  
Tokyo, Japan, 101-8430  
{ymizuta, collier}@nii.ac.jp 
 
Abstract 
Information extraction (IE) in the biomedical 
domain is now regarded as an essential 
technique for the dynamic management of 
factual information contained in archived journal 
articles and abstract collections. We aim to 
provide a technique serving as a basis for pin-
pointing and organizing factual information 
related to experimental results. In this paper, we 
enhance the idea proposed in (Mizuta and 
Collier, 2004); annotating articles in terms of 
rhetorical zones with shallow nesting. We give a 
qualitative analysis of the zone identification 
(ZI) process in biology articles. Specifically, we 
illustrate the linguistic and other features of each 
zone based on our investigation of articles 
selected from four major online journals. We 
also discuss controversial cases and nested zones, 
and ZI using multiple features. In doing so, we 
provide a stronger theoretical and practical 
support for our framework toward automatic ZI. 
1 Introduction 
Information extraction (IE) in the biomedical 
domain is now regarded as an essential technique 
for utilizing information contained in archived 
journal articles and abstract collections such as 
MEDLINE. Major domain databases often contain 
incomplete and inconsistent results. Also, a 
majority of the reported experimental results are 
only available in unstructured full-text format. 
These being combined, scientists need to check 
with source journal articles to obtain and confirm 
factual information. Furthermore, they often need 
to start with document retrieval and face an 
overwhelming number of candidate articles. Thus, 
the significance of dynamic management of factual 
information, specifically an integration and update 
of experimental results, is self-evident. It would 
not only save researchers much time used for 
retrieval and redundant experiments but also help 
them use the information more effectively. Given 
the limitations of manual work in terms of both 
efficiency and accuracy, IE focusing on factual 
information is of critical importance. 
Researches in bioNLP have made major 
progress mainly in the extraction of bio-named 
entity biological interactions (e.g. Craven et al. 
1999; Humphreys et al., 2000; Tanabe et al., 2002). 
But further progress aimed at pin-pointing and 
organizing factual information remains a challenge. 
We aim to provide a basis for this purpose. As 
the first step, we proposed in (Mizuta and Collier, 
2004) annotating biology texts in terms of 
rhetorical zones with a shallow nesting, and 
provided an annotation scheme. In this paper, we 
explore a qualitative analysis of zone identification 
(ZI) in biology articles and provide stronger 
support for our framework toward automatic 
annotation of zones. Specifically we; 1) illustrate 
the linguistic and other features of each zone, 
which have been extracted through our pilot study 
of a total of 20 articles randomly selected from 
four major online journals (EMBO, PNAS, NAR 
and JCB), 2) discuss controversial cases for ZI and 
nested annotation to elaborate the scheme, 3) 
discuss multiple features relevant to ZI, and 4) 
summarize the investigation and outline future 
steps related to machine learning and applications. 
Previous work on rhetorical analysis of scientific 
articles focus on either; 1) hierarchical discourse 
relations between sentences (e.g. Mann and 
Thompson, 1987), 2) genre analysis within a 
descriptive framework (e.g. Swales 1990), or 3) ZI 
in a flat structure and a statistical evaluation of the 
annotation scheme from a machine learning 
perspective (e.g. Teufel and Moens, 2002). We 
follow the lines of (Teufel and Moens, 2002) and 
apply ZI to the domain of biology. But our 
approach is unique in that we focus on 
experimental results and on a qualitative analysis 
of ZI as a basis for automatic ZI. 
2 Overview of the framework 
2.1 The need for zone identification (ZI) 
We discuss below the critical issues in bioNLP 
involved in pin-pointing and organizing factual 
information and show how ZI can be applied. 
First, articles provide information in various 
rhetorical statuses (e.g. new vs. old results; own vs. 
previous work). Current IE relies on surface lexical 
29
and syntactic patterns, neglecting the rhetorical 
status of information. Thus, we are in danger of 
extracting old results mixed with new ones. 
(1) Recent data suggest that … ~ is involved in 
DPC removal in mammalian cells (ref.), … 
…The data presented here suggest that … 
The data  (1) provide statements in different 
rhetorical statuses (boldfaced by us). Preprocessing 
the text in terms of such information helps filter 
out old results (i.e. the first statement). 
Secondly, so far the scope of bioNLP largely 
bear on abstracts. But arguably, the final goal 
should be full texts, given their much richer 
sources of information and the increasing ease of 
access (e.g.  open access to collections such as 
PUBMED-central; online journals such as EMBO, 
PNAS, and JCB). This involves exploring new 
techniques because there are some essential 
differences from abstracts. Among others, full texts 
present much more complexity in the sentence 
structure and vocabulary (e.g. inserted phrases, 
embedded sentences, nominalization of verbs, 
more anaphoric expressions). Thus, we expect that 
the analysis of the whole text requires a much 
more complex set of patterns and algorithms,
1
 
resulting in errors. A solution to this problem is to 
identify the subset of the article relevant to further 
analysis at issue. For example, in order to extract 
certain kinds of biological interactions found by 
the author, we could skip statements about 
previous work as seen in the Introduction section. 
Thirdly, experimental results make sense in their 
relation to the experimental goal and procedure. 
Also, there are usually a sequence of experiments 
performed, each of which obtains complex results. 
Therefore, it is important to extract a set of 
experimental results in an organized manner. This 
also helps identify the reference of demonstratives 
(e.g. this) and pronouns (e.g. it). 
From these points of view, ZI in articles plays an 
essential role in extracting factual information of 
different sorts from different zone classes. 
2.2 Characteristics of the framework 
The idea underlying ZI in our sense contrasts 
with other, discourse relations-based notions (e.g. 
Mann et al. 1987; Kando 1999; van Dijk, 1980); 
we focus on the global type of information. For 
example, in our ZI, reference to previous work as 
background information remains as such whether it 
is supported or refuted by the author later in the 
article, whereas this difference plays an essential 
role in discourse relations-based analyses. 
                                                      
1
 A. Koike (at AVIRG 2004) reported that to extract 
the interactions between two biological elements from 
PUBMED abstracts, about 400 patterns were necessary. 
The larger picture we have consists of 2 levels;
 
 
1) ZI, and 2a) analysis of zone interactions (e.g. 
discourse relations), or 2b) analysis on specific 
zones (i.e. extraction of biological interactions). In 
this paper we focus on the first step. 
2.3 Annotation scheme 
Our annotation scheme is proposed in (Mizuta et 
al., 2004), based on Teufel et al.’s (2002) scheme. 
Three major modifications are made; 1) a fine-
grained OWN class based on the model of an 
experimental procedure which we identified across 
journals, 2) CNN and DFF classes to cover the 
relations between data/findings, and 3) nested 
annotation. The set of zone classes is as follows: 
• BKG (Background): given information 
(reference to previous work or a generally 
accepted fact) 
• PBM (Problem-setting): the  problem to be 
solved; the goal of the present work/paper. 
• OTL (Outline): a characterization/ summary of 
the content of the paper. 
• TXT (Textual): section organization of the paper 
(e.g. “Section 3 describes our method”). 
• OWN: the author’s own work: 
◊ MTH (Method): experimental procedure; 
◊ RSL (Result): the results of the experiment; 
◊ INS (Insight): the author’s insights and findings 
obtained from experimental results (including 
the interpretation) or from previous work 
◊ IMP (Implication): the implications of 
experimental results (e.g. conjectures, 
assessment, applications, future work) or those 
of previous work 
◊ ELS (Else): anything else within OWN. 
• CNN (Connection): correlation or consistency 
between data and/or findings.  
• DFF (Difference): a contrast or inconsistency 
between data and/or findings. 
The basic annotation unit is a sentence, but in 
some cases it may be a phrase. In light of those 
cases which fit into multiple zones, we employ 2-
level annotation. Empirical analysis indicates that 
even though zone classes are conceptually non-
overlapping, an annotation unit may fit into 
multiple classes. That is, a linguistic unit (e.g. a 
sentence) may well represent complex concepts. 
Therefore, we consider that nested annotation is 
necessary, even though it complicates annotation. 
3 Zone identification -1: Main features of 
each zone 
Based on our sample annotation of full texts, we 
discuss the major features extracted from the data 
30
of each zone class. Complex cases and the location 
of zones will be discussed in later sections. 
3.1 BACKGROUND (BKG) 
(1) In cells, DNA is tightly associated with … 
(2) Ref. suggested/ suggests that ~  
(3) A wide variety of restriction-modification (R-
M) systems have been discovered …. 
BKG has three tense variations; 1) simple 
present for a generic statement about background 
information (e.g. biological facts; reference to 
previous work), 2) simple past, and 3) present 
perfect, to mention the current relevance of 
previous work. A wider range of verbs are used to 
cover both biological and bibliographical facts. 
Citations in the sentence-final position having as 
its scope the whole sentence signal BKG, but inter-
sentential citations having a smaller scope do not. 
3.2 PROBLEM SETTING (PBM )  
There are two types of PBM. 
(2) X has not been established/addressed 
there has been no study on X 
little is currently known about ~ 
there are very limited data concerning X 
X remain unclear 
The first type as illustrated above is observed in 
the I-section
2
; it addresses the problem to solve. It 
has a ‘negative polarity’ in that it mentions 
something missing in the current situation (e.g. 
knowledge, study, a research question). It contains 
vocabulary expressing negation or incompleteness 
(boldfaced). Tense variation is either simple 
present or present perfect, depending on the 
temporal interval referred to. The range of verbs 
used has not been analyzed yet. 
(3) To test {whether ~ / this hypothesis/…},  
To evaluate X; To address the question of X  
The second type of PBM is observed in the R-
section. As illustrated in (3), it corresponds to a to-
phrase appearing sentence-initially or finally. It is 
combined with a description of experimental 
procedure, as illustrated in (8).  
The two types of PBM are both related to a goal 
description. The first type concerns the whole work 
and the second type its subset (i.e. an experiment). 
3.3 OUTLINE (OTL) 
(4) We report here the results of experiments…. In 
brief, we have asked, … To address the first 
question, we utilized … We found … Together, 
these results not only confirm that …. but also 
that… (End of the I-section) 
                                                      
                                                     
2
 In what follows, I-, M-, R-, and D- section stand for 
Introduction, Method and Materials, Results, and 
Discussion sections, respectively  
OTL provides a concise characterization of (or 
an ‘excerpts’ from) the work as an abstract does. 
(5)  [Introduction Body Conclusion]
full-text article
 
The rhetorical scheme of the whole article is 
analyzed as (5). OTL has as its scope “Body”, and 
thus it is expected to appear either in Introduction 
or Conclusion. This conforms to our investigation. 
Tense choices are between simple present and 
future (in Introduction), and between present 
perfect and simple past (in Conclusion). 
The first element of (4) signals the beginning of 
an OTL zone. By itself it would fit into AIM (of 
the paper) employed in (Teufel et al., 2002). It 
contains certain kind of linguistic signals such as:
3
 
(6) Indexicals:  
e.g. in this paper; in the present study; here 
‘Reporting verbs’ or verbs for presentation:  
e.g. we show/ demonstrate/ present/ report 
However, OTL consists of a wider range of 
sentences. As illustrated in (4), OTL also contains 
those elements which provide information relevant 
to other zones (e.g. PBM, MTH and RSL). We 
consider that the whole sequence of sentences in 
(4) deserve an independent class from both 
theoretical and practical perspectives. That is, it is 
embedded in a reporting context, and provides 
abstract-like information. Thus, we propose OTL. 
3.4 TEXTUAL (TXT) 
TXT zones were not observed in our sample. 
This makes sense because the journals investigated 
provide a rigid section format. However, we retain 
this class for future application to other journals 
which may provide a more flexible section format. 
3.5 METHOD (MTH) 
(7) we performed X , using …; we exploited the 
presence of ~; we utilized sucrose-gradient 
fractionation; X was normalized 
MTH takes the form of an event description in 
the past tense, using matrix verbs expressing the 
experimental procedure (e.g. perform, examine, 
use, collect, purify). Either a passive or an active 
form (with we as its semantic subject) is used. 
(8) [To test ~,]
 PBM
  [we performed ~]
 MTH
. 
We observed that a paragraph in the R-section 
starts with a combination of PBM and MTH as 
illustrated in (8). It is much more common for 
PBM to come first. This can be explained in terms 
of ‘iconicity’, the phenomenon that the conceptual 
and/or the real world ordering of elements is often 
reflected in linguistic expressions. In (8), the PBM 
 
3
 For a more comprehensive set of expressions, see, 
for example, (Swales, 1990) and (Teufel et al., 2002). 
31
portion (to-phrase) is preposed conforming to the 
fact that the author first had the experimental goal. 
3.6 RESULT (RSL) 
(9) the distribution of ~ was shifted from …; 
no significant change was seen; 
cells … demonstrated an enrichment in ~ 
RSL usually describes an event in the past tense, 
as MTH does, using a certain set of verbs 
expressing; 1) phenomena (e.g. represent, show 
and demonstrate,  having as its subject the material 
used), 2) observations (e.g. observe, recognize and 
see, having we as its subject, or in the passive 
form), or 3)  biological processes (e.g. mutate, 
translate, express, often in the passive form).  
(10) the distribution of ~ is shifted from … 
no significant change is seen 
cells devoid of Scp160p demonstrates ~ 
~ are presented in Table 2. 
As illustrated above, RSL, unlike MTH, may 
also be written in the present tense to create a 
context in which the author observes and presents 
the results real-time,  referring to figures. 
In the R-section, RSL zones were observed to 
follow MTH with no discourse connectives. 
However, the boundary was rather easy to identify, 
by virtue of a cause-effect relation identified. 
Specifically, matrix verbs used in these zones 
played a critical role; some of them present a rather 
complementary distribution. This feature is useful 
for machine learning too.
4
 
MTH and RSL may be combined by resulted in: 
(11) [Parallel … transcription reactions using…]
 MTH
 
resulted in [… strong smears. ]
 RSL
 
However, result in is usually observed in relating 
biological events, and the above usage relating a 
method and results is found uncommon. Also, the 
explicit use of result(s) as below is uncommon: 
(12) The results, ……, were striking. First, …  
Given these, keyword searches using result(s) do 
not work for the purpose of identifying 
experimental results. In contrast, RSL zones can be 
identified using features such as matrix verbs and 
location. Thus, annotating RSL zones is important. 
(13) Interestingly/ Surprisingly/ Noticeably/…,  
In a RSL zone, empathetic expressions as in (13) 
may be used, often sentence-initially, to call the 
reader’s attention. The adjective version (e.g. 
striking in (12)) is also used. 
                                                      
                                                     
4
 The occurrences in MTH/ RSL in our sample were: 
perform 38/2, use 181/12, collect 10/1, purify 23/2, 
observe 1/43, reduce 1/15, affect 1/15, associate 6/25. 
However, some verbs had a rather neutral distribution 
(e.g. detect 11/13, follow 26/8). Such cases require the 
use of other features too, as we will discuss later on. 
3.6.1 INSIGHT (INS) 
We have identified three major patterns for INS. 
The examples below illustrate the first pattern: 
(14) [As can be seen in Figure 2C, … was not 
significantly different compared
 
with that in 
Figure 2A,]
RSL
 [indicating that … had no 
appreciable effect on ….]
 INS
 
(15) [Interestingly, central ZYG-9 was significantly 
reduced in … embryos ……In the converse 
experiment, … was observed in embryos…. ]
RSL
 
[These results suggest that γ -tublin is required 
to assemble centrosomes ……]
 INS
 
These are conventionalized forms which the author 
uses in stating his/her interpretation of the results 
with respect to a biological process behind the 
observed results. A generalization is:
5
 
(16) X indicate Y        (a variant: X, indicating Y  ) 
X: results/experiments/studies, 
Y: biological statement or model,  
Verb variations from our sample: 
indicate/suggest/demonstrate/represent/reveal. 
The second pattern is a sentence using the verb 
seem/ appear or consider such as: 
(17) X seem/appear to V (It seems/ appears that ~) 
X is considered to V 
The third pattern is the use of confirm/ support: 
(18) This was confirmed, as shown in Figure 3. 
Here, this refers to the author’s hypothesis. 
Although (18) refers to a figure which shows the 
result, the sentence does not fit into RSL but into 
INS. We consider that it describes the author’s 
interpretation of the result and that the hypothesis 
is now licensed as an insight. A generalization is: 
(19) X confirm that Y;  Y was confirmed. 
X: results/experiments/studies 
Y: proposition (hypothesis or prediction). 
As we will discuss later, confirm also signals 
CNN, relating two things (X and Y). Therefore, it 
triggers a nested annotation for INS and CNN. 
3.7 IMPLICATION (IMP) 
The IMP class is used as a cover category for the 
author’s ‘weaker’ insights from experimental 
results and for other kinds of implication of the 
work (e.g. assessment, applications, future work). 
(20) Fusion of …of type III enzymes, …, would 
result in  type IIG enzymes… 
(21) We speculate that as ~  lose …, ~ increases. 
‘Weaker’ insights (vs. ‘regular’ insights fitting 
into INS) are signaled by; 1) modal expressions 
 
5
 In our data, suggest occurred mainly in INS (63%) 
and BKG (23%), and indicate in INS(55%), RSL(20%) 
and MTH (10%). This means that these verbs strongly 
signal INS but other features are also needed for ZI (e.g. 
location, zone sequence, and the subject of the verb). 
32
(e.g. could, may, might, be possible, one possibility 
is that) and 2) verbs related to conjecture (e.g. 
speculate, hypothesize), as in the examples above. 
(22) These data are significant because … 
(23) This approach has the potential to increase … 
(24) ~ provides structural insights into ~ 
Assessment is signaled by weak linguistic clues 
as illustrated in (22) - (24) above. 
(25) Potential targets also remain to be studied; 
we do not yet know 
(26) Further experiments will focus on ~; 
a future study/work/challenge… 
Taken out of context, IMP mentioning future 
work look very similar to PBM as in (25), unless it 
contains key words such as future and further, as in 
(26). The critical feature for the distinction 
between them is the section in which they appear. 
3.8 ELSE (ELS) 
We found only few cases of ELS in our data. 
The following is an example (a naming statement). 
(27)  …, we refer to this gene as gip-1 and ~ as … 
The lack of ELS zone in our data indicates that 
the domain of experimental biology has a more 
established methodology and that the focus is on 
the experiments and the findings obtained. In other 
domains where the methodology is less 
standardized (e.g. computer science), we would 
expect some essential cases fitting into ELS (e.g. 
the author’s proposal and invention) and thus 
further elaboration of classes would be needed. 
3.9 DIFFERENCE (DFF) 
(28) [ [ These effects are significantly different 
from the effects caused by …]
DFF 
]
 RSL
 
(29) [ [ Our structural results differ somewhat from 
the previous proposal (ref.) and …]
DFF 
]
INS
 
As in (28) and (29), DFF is signalled by a limited 
set of vocabulary (mainly, different and contrast 
and their variants). Also, as illustrated above, DFF 
often overlaps with other classes (e.g. INS, IMP, 
RSL), and therefore involves nested annotation. 
3.10 CONNECTION (CNN) 
(30) This conservation further supports their 
putative regulatory role in exon skipping. 
(31) this peroxide treatment experiment was 
consistent with
 
previous data 
(32) The results also confirm the recent
 
discovery of 
MntH … (ref). 
(33) This conclusion was supported not only by 
… but also by … 
The CNN class covers statements mentioning 
consistency (i.e. some sort of positive relation) 
between data/findings. A generalization is: 
(34) X is consistent with Y ; X conform to Y 
X is {similar to/ same as� Y ; X support Y  
X/Y: previous work, the author’s observation,  
model, hypothesis, insight, etc. 
(35) X. Similarly, Y. (X/Y: a proposition) 
The specific relation mentioned shows a variety 
(e.g. correlation or similarity; support for the 
author’s own or other’s data/ idea/ findings). 
6
 
Interestingly, we observed more CNN zones 
than DFF zones in our sample (Mizuta et al., 2004), 
and we consider that this is not accidental; this 
asymmetry indicates that biologists put more focus 
on correlation between two elements.
7
  
4 Zone identification -2: elaboration 
4.1 Nested zones for complex concepts 
The following examples illustrate complex zones 
motivating nested annotation: 
(36)  [ [Similar DNA
 
links were also observed in the 
complexes with … (ref.), which show structural 
similarities
 
with….]
 CNN  
]
 RSL
 
(37) [ [Previous 
113
Cd NMR studies on … indicated 
that zinc plays a catalytic role.]
BKG 
[According 
to the mechanism we propose, Zn
2+
 plays a 
crucial role only in….]
INS
 ]
DFF
 [Another 
difference from the previous proposal is…]
DFF
 
Sentence (36) provides a result and compares it 
with other results (boldfaced). Thus, the sentence 
fits into RSL and CNN simultaneously; it is a case 
of combined zones, conceptually distinct from 
indeterminacy between two zones. Sentence (37) 
illustrates an example of nested zones. The first 
two sentences fit into BKG and INS respectively. 
Also, they contrast with each other, with respect to 
the role which zinc is claimed to play, deserving of 
DFF as a whole (but there is no explicit clue at this 
point). The key word in the third sentence, another 
difference (boldfaced)  licenses the sentence to 
DFF and also indicates an element referring to a 
difference already mentioned. Accordingly the first 
two sentences will be annotated for DFF. 
Precisely speaking, combined zones and nested 
zones are not identical. But we treat combined 
zones as a special case of nesting, as two zones 
having the same scope and an arbitrary ordering. 
Importantly, nested zones (in a wider sense) are 
conceptually distinct from ambiguity between two 
zones; the sentences simultaneously fit into 
                                                      
6
 DFF and CNN classes cover a wide range of 
relations between data and findings. 
7
 This insight was checked with a biologist. This 
asymmetry also suggests the essential difference 
between the biology and the computer science domains. 
In the scheme by (Teufel et al., 2002) focusing on 
computer science articles, CONTRAST seems to be 
more important than BASIS. 
33
multiple zones. In fact, in our sample, most CNN 
and DFF zones overlap with another zone such as 
INS and IMP. Since CNN and DFF zones are 
important for our purpose, we consider that nested 
annotation is necessary. 
4.2 Controversial cases  
(38) However, it was not evident whether DPCs 
composed of … were … or protelytic 
degradation was involved in the process. 
A PBM zone (in I-section) and an IMP zone 
describing future work (or limitations) often look 
very similar on the surface, as illustrated in (38), 
which is the last sentence in the article describing 
the limitation of the work presented. A critical 
feature is the location; PBM in this use is located 
in the I-section, whereas IMP in other sections. 
A PBM zone in I-section (e.g. X remains 
unclear) is considered to be a subset of a larger 
BKG zone when the problem mentioned is a 
generally accepted fact. However, we chose to 
avoid nested annotation in this case, because; 1) 
the situation above is rather common, and yet 2) 
we identify the significance of PBM zone in its 
own. In case a single sentence consists of a clause 
fitting into BKG and another fitting into PBM, 
then it will result in a complex annotation. That is, 
we annotate the sentence as both BKG and PBM. 
5 Zone identification -3: location 
We now analyze the zones appearing in each 
section and their sequence, to try to describe the 
locations where a specific zone class may appear. 
The section organization of the sample articles is 
mapped onto the scheme shown in (5) as follows:
8
 
(39) [I
Intro
 [M R D(non-final)]
Body
 D(final)
 Conc
] 
In what follows, I, M, R, and D stand for the 
corresponding section. 
5.1 I-section and M-section 
Common to all sample articles, the I-section 
consists of a large number of BKG zones with a 
few PBM zones inserted in it, which is then 
followed by an OTL zone. The OTL zone may or 
may not constitute a separate paragraph. 
The M-section focuses on methodological details, 
and thus consists of MTH zones possibly with an 
ignorable number of other zones (e.g. BKG, INS).  
5.2 R-section 
The R-section consists of ‘problem-solving’ 
units following the experimental procedure. The 
main elements of each unit are PBM, MTH, and 
RSL zones, which are often then followed by an 
                                                      
                                                     
8
  Or, [I
Intro
 [R D(non-final) M ]
Body
 D(final)
 Conc
] 
INS zone. There are also some optional elements. 
A generalization of the zone patterns is as follows. 
For practical reasons, we use the regular 
expression style; superscripts + and * stand for the 
occurrence of one (+) / zero (*) or more times. 
Brackets represent OR-relation. 
(40) ( X
*
  PandM  MTH
+
  (RSL  INS
*
  IMP
*
)
* 
)
+
 
X:  an arbitrary zone, and 
PandM = [ (PBM MTH)  (MTH PBM) ] 
Below are examples of an optional zone (X) 
placed at the beginning a problem-solving unit:
9
 
(41) [It is possible that … ]
IMP 
[To test this 
possibility,]
PBM
 [we examined …]
 MTH 
 
(42) [... has revealed two motifs (Fig. 1). As can be 
seen in Figure 1A, …….]
RSL 
[To ascertain that 
….]
PBM
 [ we aligned their weight …]
MTH
 
5.3 D-section 
The D-section is much more complex and 
flexible, but some generalization is possible.  
First, the essential components of D-section, 
both quantitatively and qualitatively, are INS and 
IMP zones. This indicates that the focus of D-
section is on obtaining deeper insights. In contrast 
with the zone sequence in the R-section, INS and 
IMP often precede, or even lack, RSL and BKG 
zones related to them. A closer look at examples 
explains the apparent lack of  RSL/BKG: 
(43) The data within this report demonstrate… 
(44) As for the C-rich element, its comparison with 
the PTB binding motif has shown that these are 
different motifs. 
(45) Similarly, the failure of ... protein (Fig. 7) 
suggests that... 
The italicized elements in (43) - (45) would fit into 
RSL or MTH, but are too small constituents to be 
annotated. As a result, only the whole sentence  
gets annotated as INS. A similar tendency holds 
also for BKG (e.g. since-clause), but less 
frequently. We may consider extracting these cases 
in future work. Usually D-section ends with OTL 
(summary) or IMP (assessment or future work). 
6 Zone identification using multiple features 
Table 1 illustrates multiple features contributing 
to ZI, as we identified them through our manual 
annotation. We observed that certain pairs of zone 
classes  present similar distribution of key features, 
with the same primary feature, and that BKG lacks 
a key feature, indicating its neutral nature. Using 
multiple features is critical in ZI. We intend to 
 
9
 We observe that these paragraph-initial zones 
trigger the PBM zone. For example, this in (41) refers to 
the preceding IMP zone, and the RSL in (42) mentions 
the results of a preceding experiment. 
34
improve our insight shown here through 
quantitative analysis (cf. fn. 3 and 4). It then better 
helps determine the right set of features and their 
relative priority to be used in machine learning. 
 
Feature\ Zone  
B   P  O   M R  INS IMP  CNN DFF
lexical/syntactic -     -  -  �    �      �    �  
matrix verb -   �� ��  -      -        -     -  
location -   �� �           �  �  
zone sequence -    �    �       �  �  
reference to Fig x   -   -   ��  x    x        x    x 
citation   -   -    -  x   x    x       �   �  
Table 1: Multiple features for ZI  
Explanatory notes on the priority of features: 
� : primary feature (with specific clues); 
 : major feature;  �: secondary feature 
x:  negative feature;  -: non-/less informative 
7 Conclusion 
We have provided a qualitative analysis of the 
process and results of ZI based on our hand-
annotated sample, with a view to strengthening the 
basis for the annotation scheme. We are now 
starting to use our sample as training data for 
machine learning, as well as creating more data in 
a systematic way, toward automatic annotation. 
We are also considering to use our ontology 
management tool (Open Ontology Forge, 
http://research.nii.ac.jp/~collier/resources/OOF/ind
ex.htm) for these purposes; 1) to define zone 
classes as ontology classes; zone annotation is then 
expected to be a variant of named entity annotation, 
which we are familiar with, and 2) to link between 
expressions referring to results (e.g. these results/ 
our results) and their antecedent (i.e. the RSL zone 
providing a concrete description of the 
experimental results), using the coreference tool. 
Applications include full color representation of 
annotated texts; a sample is available at: 
http://research.nii.ac.jp/~collier/projects/ZAISA/in
dex.htm. Also, IR focusing on particular zone 
classes should improve the quality of retrieval. 
Specifically, the goal of the experiment (a PBM 
zone) is expected to be used as an index for the 
organization and retrieval of experimental results. 
Acknowledgements 
We gratefully acknowledge the kind support of 
our colleague Tony Mullen with the quantitative 
analysis of the data, the generous support of 
Professor Asao Fujiyama (NII) and the funding 
from the BioPortal project, and the very helpful 
comments from the three anonymous reviewers. 

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