Annotating Cohesive Statements of Anatomical Knowledge Toward

Semi-automated Information Extraction

Kazuo Hara

, Ikumi Suzuki

, Kousaku Okubo

and Isamu Muto

National Institute of Genetics, Mishima, Shizuoka, Japan

BITS. Co., Ltd., Chiyoda, Tokyo, Japan

Keywords:

Semi-Automated Information Extraction, Cohesive Text, Itemized Text.

Abstract:

Anatomical knowledge written in a textbook is almost completely unreusable computationally, because it is

embedded in a cohesive discourse. In discourse contexts, the frequent use of cohesive ties such as refer-

ence expressions and coordinated phrases not only troubles the function of automated systems (i.e., natural

language parsers) to extract knowledge from the resulting complicated sentences, but also affects the identiﬁ-

cation of mentions of anatomical named entities (NEs). We propose to revamp the prose style of anatomical

textbooks by transforming cohesive discourse into itemized text, which can be accomplished by annotating

reference expressions and coordinating conjunctions. Then, automatically, each anaphor will be replaced by

its antecedent in each reference expression, and the conjoined elements are distributed to sentences duplicated

for each coordinating conjunction connecting phrases. We demonstrate that, compared to the original text,

the transformed one is easy for machines to process and hence convenient as a way of identifying mentions

of anatomical NEs and their relations. Since the transformed text is human readable as well, we believe our

approach provides a promising new model for language resources accessible by both human and machine,

improving the computational reusability of textbooks.

1 INTRODUCTION

Scientiﬁc textbooks are language resources in which

the knowledge continually being accumulated by hu-

mankind is presented to readers in order to elucidate

the nature of the universe. Compared to genres of text

such as news or weblogs, which have commonly been

exploited by natural language processing researchers,

the density of valuable knowledge in textbooks is ac-

tually considerably higher, because they are a vehi-

cle for the overview of consensus knowledge care-

fully ascertained by the scientiﬁc community, packed

tightly sentence by sentence. For instance, anatomi-

cal textbooks, which we will focus on here, describe

the structures of the human body, the composition

of individual tissues, and the relations between them.

Such anatomical knowledge is fundamental for good

health care, in that medical activities such as diagno-

sis, intervention, and prognosis aim to cure structures

or tissues of the human body from ailments; hence,

detailed knowledge of those structures is needed.

However, these concentrated accumulations of

knowledge are at present almost totally unreusable

computationally. In particular, they are not suited

to automatic processing for semantic searching and

reasoning. This is because the knowledge in a text-

book is embedded in a cohesive discourse. In other

words, a textbook does not comprise a collection of

separate pieces of knowledge like subject–predicate–

object triples in a resource description framework

(RDF), which are employed in Linked Open Data

(LOD) or knowledge bases such as FreeBase and DB-

Pedia. Instead, what we do with a textbook is just read

and understand it in our mind.

Thus, it seems that isolating individual pieces of

knowledge from anatomical textbooks should be use-

ful. However, this is not a straightforward process,

because description is highly cohesive. As a result of

the exigency to reduce redundancy using cohesive ties

(that is, reference expressions, coordinated phrases),

most statements in a given context depend on each

other, and often do not make sense without reference

to each other. In fact, it has been reported that cohe-

sive ties are frequently used in scientiﬁc texts (Sch¨afer

et al., 2012). This is likely because it is thought that

the coherence yielded by the ties increases the quality

of the text (Witte and Faigley, 1981).

In order to overcome difﬁculties arising from the

342

Hara K., Suzuki I., Okubo K. and Muto I..

Annotating Cohesive Statements of Anatomical Knowledge Toward Semi-automated Information Extraction.

DOI: 10.5220/0005132303420347

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2014), pages 342-347

ISBN: 978-989-758-048-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

The sclera has received its name from its extreme density and hardness; it is a firm, unyielding

membrane, serving to maintain the form of the bulb. It is much thicker behind than in front;

the thickness of its posterior part is 1 mm. Its external surface is of white color, and is in

contact with the inner surface of the fascia of the bulb; it is quite smooth, except at the points

where the Recti and Obliqui are inserted into it; its anterior part is covered by the conjunctival

membrane. Its inner surface is brown in color and marked by grooves, in which the ciliary nerves

and vessels are lodged; it is separated from the outer surface of the choroid by an extensive lymph

space (spatium perichorioideale) which is traversed by an exceedingly fine cellular tissue, the

lamina suprachorioidea.

(a) A passage about “The Tunics of the Eye” in Henry Gray’s anatomical textbook (Gray, 1918)

• The sclera has received its name from its extreme density and hardness;

• The sclera is a firm, unyielding membrane,

• The sclera is serving to maintain the form of the bulb.

• The sclera is much thicker behind than in front;

• the thickness of The sclera’s posterior part is 1 mm.

• The sclera’s external surface is of white color,

• The sclera’s external surface is in contact with the inner surface of the fascia of the bulb;

• The sclera’s external surface is quite smooth, except at the points where the Recti and Obliqui

are inserted into The sclera;

• The sclera’s anterior part is covered by the conjunctival membrane.

• The sclera’s inner surface is brown in color

• The sclera’s inner surface is marked by grooves, in which the ciliary nerves and vessels are

lodged;

• The sclera’s inner surface is separated from the outer surface of the choroid by an extensive

lymph space (spatium perichorioideale)

• spatium perichorioideale is traversed by the lamina suprachorioidea.

• the lamina suprachorioidea is an exceedingly fine cellular tissue

(b) Itemized text

Figure 1: We propose to change the prose style of anatomical textbooks, by semi-automatically transforming (a) cohesive text

into (b) itemized text, for subsequent information extraction.

interdependence of statements in cohesive text , we

propose to change the prose style of anatomical text-

books by semi-automatically transforming cohesive

discourse into itemized text – a set of independent

statements like the one illustrated in Figure 1(b), ob-

tained by annotating reference expressions and coor-

dinating conjunctions so as to decompose their cohe-

sion.

We then validate the utility of the transformed text

for the identiﬁcation of anatomical NEs and their re-

lations, verifying the assumption that textual cohe-

sion is a major obstacle to accessing the valuable

knowledge in textbooks by means other than reading.

Speciﬁcally, we show that, compared to the original

texts, transformed ones are easier to process by ma-

chine and hence serve as a convenient way of iden-

tifying mentions of anatomical NEs as well as their

relations, speciﬁcally triple subject–predicate–object

relations. Since transformed text is human readable as

well, we propose a new model of language resources

accessible by both human and machine, improving

the computational reusability of anatomical textbooks

at less cost to the human reader.

2 RELATED WORK

Over the past few decades and increasingly in re-

cent years, a vast amount of research has been pub-

lished on information extraction (IE) from unstruc-

tured text. A series of Message Understanding Con-

ferences (MUCs) beginning in the 1980s has given fo-

cus to this research. IE researchers have been aiming

in particular to extract structural information on re-

lations between entities, for example to identify per-

petrator, instrument, and target in an incident of ter-

rorism (MUC-4, 1992); successful results have been

reported in many domains, including opinion mining

(Abe et al., 2011) and Inﬂuenza detection (Aramaki

et al., 2011). More recently, the traditional IE ap-

proach, which pre-speciﬁes relations in the domain in

question, has given way to a new paradigm that aims

to extract arbitrary, open-domain relations in order to

scale to the size of the web for covering a large di-

versity of relations (Etzioni et al., 2008; Riedel et al.,

2013), or to generate LOD for the semantic web (Au-

genstein et al., 2012). However, both traditional and

new IE approaches focus on extracting relations from

news or weblog text, which are considered less cohe-

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343

sive (and hence easier to read) than scientiﬁc texts.

To our knowledge, however, no study has reported

IE results from highly cohesive texts like anatomical

textbooks. The objective of this paper is not to ap-

ply standard IE methodologies to cohesive text, but to

demonstrate the effect of cohesion decomposition on

IE using anatomical textbooks.

Nevertheless, the set of itemized statements that

we transform from out of the textbooks can be con-

sidered to constitute a corpus annotated for future au-

tomation of anaphora resolution and coordination dis-

ambiguation. There are, in fact, already many pub-

licly available corpora annotated for anaphora (see

(Ng, 2010)), and a few for coordination such as GE-

NIA (Kim et al., 2003). However these corpora were

constructed mainly in order to compile training data

for supervised machine learning. To our knowledge,

no previous research on corpus annotation has investi-

gated the effects of anaphora resolution and coordina-

tion disambiguation on the identiﬁcation of complex

NEs like anatomical terms and of their relations.

3 MOTIVATING EXAMPLES

3.1 Complicated Mentions of

Anatomical Named Entities

Many anatomical named entities (NEs) comprise

multiple words. For example, in FMA (The Founda-

tional Model of Anatomy) (Rosse and Mejino, 2008),

a reference ontology in the domain of anatomy, the

average number of words that an NE consists of is 6.2

over the total number of 78977 terms.

Thus, mentions of anatomical NEs tend to take

the form of long noun phrases consisting of a proper

noun and its modiﬁers, such as prepositional phrases

and adjectives. Owing to this, the identiﬁcation of

anatomical NEs is adversely affected by cohesive ties.

Because it is tedious for human writers to repeat long

terms such as anatomical NEs and for human readers

to read them, they are likely to be replaced by refer-

ence expressions upon mentions after the ﬁrst. For ex-

For example, the percentage of sentences that contain

coordinating conjunction (such as “and” and “or”), tagged

as “CC,” is 44.9% for news texts in the Wall Street Journal

part of the Penn Treebank (Marcus et al., 1993), but 58.8%

for biomedical articles in the GENIA corpus (Kim et al.,

2003), although average sentence length is almost the same

in these corpora (23.9 words for the Penn Treebank and 23.4

words for GENIA).

One reason for this is that anatomical NEs likely con-

tain prepositional phrases. For instance, the percentage of

terms in FMA that contain the preposition “of” is 78.3%.

ample, if the lateral sulcus was previously mentioned,

an anatomical NE consisting of six words,

anterior horizontal limb of the lateral sulcus

might often be given as

anterior horizontal limb of the sulcus,

where lateral sulcus is replaced with its anaphor the

sulcus. The resulting term will no longer be identiﬁ-

able by dictionary look-up.

Moreover, when two NEs share words, like

anterior horizontal limb of lateral sulcus and

anterior ascending limb of lateral sulcus,

a coordinating conjunction may be used to reduce re-

dundancy, as for instance

anterior horizontal and ascending limbs of

lateral sulcus.

As indicated above, this manner of writing anatomical

terms prevents the identiﬁcation of mentions of these

NEs by exact-match look-up in the dictionary.

3.2 Complicated Sentences

In anatomical textbooks, cohesive ties not only occur

frequently but also make up complex reference chains

and nested coordinations. For example, the two

anaphors (bolded) in the next sentence are chained:

The thalami are two large ovoid masses. The ante-

rior extremity is narrow. It lies close to the middle

line... (Gray, 1918)

Because It refers to The anterior extremity and The

anterior extremity refers to The anterior extremity of

the thalami,

by summing up the two effects, we see

that It actually refers to The anterior extremity of the

thalami.

Another example is the following sentence con-

taining two coordinated phrases, induced by conjunc-

tion and, that are nested:

The posterior extremity is expanded, is directed

backward and lateralward, and overlaps the supe-

rior colliculus. (Gray, 1918)

Nested coordination makes a sentence more complex

and longer, and thus more difﬁcult to parse (Hara

et al., 2009). This causes a problem, namely the

identiﬁcation of relations as subject–predicate–object

structures on the basis of erroneous parser outputs.

In Figure 2, instead of considering that The anterior

extremity refers to The anterior extremity of the thalami,

we suppose for convenience that The refers possessively to

Thalami.

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Figure 2: A web GUI we developed for the annotation of reference expressions and coordinating conjunctions. The ﬁrst and

second columns denote sentence IDs and original texts. The third column, which is empty at the beginning, shows annotations

added by a worker. The fourth column shows texts transformed according to the annotations.

4 DEMONSTRATION

We now transform an anatomical textbook into item-

ized text; then, we validate the utility of the trans-

formed text for the identiﬁcation of anatomical NEs

and their relations.

We use “Anatomy of the Human Body”, a textbook

written by Henry Gray (Gray, 1918). We select this

textbook because although the anatomical knowledge

presented in it was mostly established in earlier days

and is now in the public domain, removing licensing

issues, it is nevertheless mostly current.

Gray’s textbook comprehensively describes the

morphological features of and relations among hu-

man body parts, such as the bones, muscles, nerves,

and so on. We focused on the “Fore-brain or Prosen-

cephalon” section,

which describes the most compli-

cated structure in the human body.

We split the text manually and obtained 787 test

sentences. The percentage of sentences that contain

coordinating conjunction “and” is 56.8%, and aver-

age sentence length is 23.2 words.

http://www.bartleby.com/107/189.html

4.1 Semi-automated Itemization

4.1.1 Manual Annotation.

Using the web GUI shown in Figure 2, Gray’s text

is annotated to allow the decomposition of the co-

hesion induced by reference expressions and coordi-

nated conjunctions. Annotations are done by a worker

using a computer mouse to select from the origi-

nal texts two or more sequences of words (e.g., an

anaphor and its antecedent, or a coordinating conjunc-

tion and its conjoined elements) and link them with a

label, according to the two tasks below:

Anaphora Resolution. For each anaphor (whether

an anaphoric pronoun or an anaphoric noun phrase)

in the original texts, select its antecedent, and connect

anaphor and antecedent with a label “REFERS TO.”

In the GUI, this annotation is denoted as

anaphor REFERS TO antecedent.

Note that for indirect anaphora in which a possessive

pronoun or anaphoric determiner is involved, a dif-

ferent label is selected, “REFERS POSSESSIVELY

TO.”

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The Thalami are two large ovoid masses.

Thalami's anterior extremity is narrow.

Thalami's anterior extremity lies close to the middle line.

Thalami's anterior extremity forms the posterior boundary of the interventricular foramen.

Thalami's posterior extremity is expanded.

Thalami's posterior extremity is directed backward.

Thalami's posterior extremity is directed lateralward.

Thalami's posterior extremity overlaps the superior colliculus.

(a) Transformed text

< Thalami's anterior extremity, form, posterior boundary of interventricular foramen >

< Thalami's posterior extremity,

overlap, superior colliculus >

(b) Relations (<subject, predicate, object>) extracted from transformed text

Figure 3: Example of relation extraction from transformed texts (using the same texts as in Figure 2.)

Coordination Disambiguation. For each occur-

rence of a coordinating conjunctions such as and, ﬁnd

a series of elements (verbal phrases) that are con-

joined by the conjunction, and mark the whole with

a label “CONJOINS.” In the GUI, the annotation is

denoted as

coordinating conjunction CONJOINS a series

of elements.

Across the 787 test sentences, we attached 507

“REFERS TO” labels, 165 “REFERS POSSES-

SIVELY TO” labels, and 783 “CONJOINS” labels.

4.1.2 Transformation

When text is annotated, transformation of the origi-

nal text can be performed automatically according to

the annotation. With reference to the “REFERS TO”

labels, original texts are transformed by replacing the

anaphors with their (possibly chained) antecedents (or

with an apostrophe plus the letter s for the “REFERS

POSSESSIVELY TO” labels). For an original sen-

tence to which was attached a “CONJOINS” label,

the sentence are duplicated and each of the conjoined

elements is distributed. For example, the third and

fourth sentences in Figure 3(a) are generated by dupli-

cating the third sentence of the original text in Figure

2, which contains one coordinated phrase.

Ultimately, we transformed the original 787 sen-

tences into a set of itemized statements constituting

1871 context-independent sentences.

4.2 Information Extraction

Now we compare the original and the transformed

text with respect to the results for the identiﬁcation

of mentions of anatomical NEs and their subsequent

relation extraction.

Table 1: The number of mentions of anatomical NEs iden-

tiﬁed from 787 test sentences, before (= original text) and

after (= transformed text) decomposing cohesion.

# anatomical NEs diff.

Original text 1641 —

Transformed text

2194 +553

Mention Identiﬁcation. To identify the anatomical

NEs mentioned in the text, we looked them up in

FMA,

a reference ontology of anatomy (Rosse and

Mejino, 2008). Table 1 shows the number of identi-

ﬁed mentions of anatomical NEs, and indicates that

this number was increased by the decomposition of

cohesion.

This is partly because the 15 anatomical

NEs listed in Table 2 in the transformed text that are

missing in the original text are discovered by dictio-

nary look-up.

Relation Extraction. Next, we employed Enju,

state-of-the-art natural language parser, and applied it

to the text. Speciﬁcally, we parsed the original and

the transformed text, and from among the subject–

predicate–object structures output by Enju, we picked

up those that contained anatomical NEs (identiﬁed

by the previous process of mention identiﬁcation) in

both the subject and the object. Table 3 shows the re-

sult. The number of extracted relations, as well as the

number of those that were anatomically correct,

was

greatly increased by transformation. For example, we

http://www.berkeleybop.org/ontologies/fma.obo

We do not count duplicate mentions of NEson the basis

of the “CONJOINS” labels.

http://www.nactem.ac.uk/enju/

The correctness was judged by a medical doctor.

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Table 2: Anatomical NEs that are missing in the original

text but are discovered in the transformed text by dictionary

look-up.

FMA ID Anatomical Term Name

50087 Anterior choroidal artery

50655

Calcarine artery

52573

Inferior branch of oculomotor nerve

59669

Roof of internal nose

61944

Anterior forceps of corpus callosum

62418

Lateral orbital gyrus

67956

Medial longitudinal stria

83759

Anterior ascending limb of lateral sulcus

83760

Anterior horizontal limb of lateral sulcus

83761

Posterior ascending limb of lateral sulcus

84114

Apical part of cell

256305

Lateral surface of cerebral hemisphere

256312

Basal surface of cerebral hemisphere

256318

Medial surface of cerebral hemisphere

256335

Tentorial surface of cerebral hemisphere

Table 3: The number of relations (subject–predicate–object

triples that contain anatomical NEs in both subject and ob-

ject) extracted from 787 test sentences, and the number of

those that were anatomically correct, before (= original text)

and after (= transformed text) decomposing cohesion.

# triples # correct

Original text 70 45

Transformed text 366 310

can extract the relations shown in Figure 3(b) from

the transformed texts, but not from the original ones.

5 CONCLUSION

In this paper, we proposed to transform the prose style

of anatomical textbooks by annotating reference ex-

pressions and coordinating conjunctions. We then

validated the utility of the transformed text for the

identiﬁcation of anatomical NEs and their relations,

and veriﬁed that the cohesiveness of the text is one

of the major obstacles preventing us from accessing

knowledge in textbooks by methods other than read-

ing.

Since the transformed text is human readable as

well, the proposed style has potential to serve as a

new-model language resource that is accessible by

both human and machine, promising to improve the

computational reusability of anatomy textbooks. We

also believe the proposed method to be applicable to

texts in domains other than anatomy, as long as they

mainly consist of factual explanation of structures, for

instance natural or artiﬁcial geographical and geolog-

ical features.

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