Cross-lingual Correspondences of Terms in Texts and Terminologies:
Theoretical Issues and Practical Implications
Kyo Kageura
The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japankyo@p.u-tokyo.ac.jp
Abstract
Terms are items in language that represent concepts. This relation of representation does not change through use. As such, terms have aunique status in language, second only to proper names. Due to this, clarifying the identity of concepts represented by terms becomes animportant issue at the level of what is represented, and control of terms representing the same concept also becomes an important issue atthe level of representation. These problems with which terminologists are concerned, though not clear at first glance, are in fact relevant
to general words and vocabulary to a lesser extent. In this paper I first clarify theoretical issues of terms and terminologies and what they
imply for terminology processing in particular and lexical and lexicological processing in general. I then pick up some terminologicalapplications, examine their status and suggest a few issues that can be addressed in terminology processing.
Keywords: Terminology, Concept, Comparability

1.

Background1.1. Concepts, Knowledge and Terminology
Let me start this paper with a rather theoretical discussions.
Forgeries do not destroy science. Science is destroyedwhen people, including “scientists,” start regarding claims
and “arguments” based on forged or fake data as part of science. That we can safely assume that the concept and actof science, in its proper sense, exists and is shared enablesus, not only practically but also logically, to identify what
are to be identified as forgeries as forgeries.
An argument homomorphic to this holds for the changes
in the meaning of words in general. When we say a wordchanges its meaning in accordance with its use, we logically presuppose the existence of the identity of meaning of
the word. Otherwise we cannot talk about the meaning or ameaning or meanings of a word in the first place. This logical identity indeed restricts the practical range of changes
in the meaning of a word: whenever I have responded “oh,yes, the meaning of a word is sweet and tasty, but it’s too
expensive” to a person who has asserted that “the meaningof a word changes in use,” they have always been puzzled.
In other words, the meaning of a word does not change beyond a certain limit, which reflects, at least within a certain
range of duration, the identity of the meaning. One can say
with confidence that the meaning of a word changes as longas – and precisely because – the underlying identity of themeaning of a word remains intact.
While this identity of the meaning tends to work implicitly
in the background in the case of general words, it is one of
the main and explicit concerns for technical terms. Crudely
speaking, it is this identity represented by a term that is referred to as a concept. Though it is not easy to recognise theessential difference between the relationship between concept and term on the one hand and the relationship between
meaning and word on the other (Kageura, 1995), especiallywhen terms and words are handled in practical setups as incompiling dictionaries or terminologies, there is a logicalnecessity for terminologists to talk about concepts repre-

sented by terms rather than meanings of terms.
What is more, this concept-term relation as distinct frommeaning-word relation constitutes a part of the essential
language infrastructure that supports social construction
and organisation, and issues related to this relation cancause practical – and sometimes serious – problems in oursocial life. A while ago, when US-based insurance companies started operating in Japan, the difference in the definition of “cancer” caused trouble in the application of insurance policies1 . As this is a cross-lingual case, it is easily
noticed that the issue is not to do with the change in themeaning in the process of use, but with the concept referred
to by corresponding terms.
Now let us consider the following example:
Responsibility accompanies freedom.
This clause is written in the draft revision to the Japaneseconstitution proposed by Liberal Democratic Party, whichis the governing party of Japan as of this writing. How
should we behave in the face of this statement? If one
adopts the stronger version of the Firthian view of meaning, one must accept that freedom should be accompanied by responsibility, although to what degree one must
accept that depends on how widespread this discourse is.
From the point of view of terminology, this statement isjust false from start to finish, simply in terms of the concept represented by the term “freedom”. Freedom includes
such passive forms of freedom as freedom from torture(Berlin, 1969). If we apply the LDP statement to the concept of freedom from torture, we end up with the following:If you do not take due responsibility, you may not
be free from torture.
This reveals the following essential fact about the concept
of “freedom”:
1

Personal communication with Professor Kazuhiko Ohe,
Graduate School of Medicine, The University of Tokyo.

That responsibility does not accompany freedom
is the sine qua non trait of the very concept of“freedom,” without which this word is nullified
and we cannot talk about “freedom” at all.
So the statement “responsibility accompanies freedom”
should not change the concept of “freedom.” If such abuseof language spreads, however, it may become impossible totalk about freedom. In such a situation, we are not talking
about the changes in the meaning of “freedom” as it becomes nothing to do with freedom if responsibility accompanies it. This is tantamount to killing the concept of freedom, and this is tantamount to killing the conditions whichenable us to maintain the concept of freedom. Incidentally,learning for human being is not related to accepting thestatement “responsibility accompanies freedom” as part ofthe determining feature of the concept of freedom, but togain a system of judgement that enables one to properly
identify this statement as false. The former is relevantly
called disciplinisation or indoctrination, which is not – andindeed is the complete opposite of – learning.
It is often the case that the concepts represented by terms
are not constitutively accessible and can only be presumed
as a regulatory ideal (Kant, 1781). In other words, the identity of the concept represented by a term may not be described fully. But this does not mean that the identity of
the concept does not exist and everything depends on usage. Reflecting this theoretical status of concepts and terms,
practical study of terminology is also concerned with theidentity of concepts.

1.2. Machine Learning/Disciplinisation
One of the standard ways of handling the “meaning” ofwords is word embedding or distributed representation ofwords. That representations obtained by word2vec enabled such operations as follows showed the power of distributed representation of words (Mikolov et al., 2013):
Madrid − Spain + France = Paris.
In the same manner, it is pointed out that the following also
becomes possible:
Doctor − Male + Female = Nurse2
We can immediately see the qualitative difference between
these two cases, i.e. the former reflects the relationships
among the meanings of these words, while the latter has
nothing to do with the meanings of “doctor,” “nurse,” “female,” or “male.” and just reflects gender biases that exist
in society and in social discourse. We can also recall what
happened to Microsoft Tay, soon started tweeting about its
admiration for Hitler and using racist slurs against Jewish
and black people. Using the term we introduced above, wehave to say that machines did not learn, but rather were disciplinised or indoctrinated3 .
Can corpus-based or data-oriented terminology processing
get around these or similar issues? We have been (mostly
unconsciously) assuming yes, for the following reasons:2
An example cited in the Q&A session for Steedman, M., “Ondistributional semantics,” invited talk at the Australian Language
Technology Association 2016 Workshop.
3
I owe this recognition to Dr. Hideto Kazawa of Google.

• Specialised knowledge is created and expressed in theproper manner, and biases are filtered out through peer
review in each specialised domain of knowledge;• Popularisation and wider dissemination of specialised
knowledge is also carried out in a due manner, reducing the granularity of discourse but essentially keeping
the wholesomeness of the specialised knowledge.
Assuming these hold, we can safely use domain corpora fora narrower or wider range for different domains in different
languages, even if machines can only be disciplinised andcannot learn in the proper sense of this word.
Unfortunately, however, a range of recent events indicatethat relying on these assumptions is becoming more andmore dangerous:• Forgeries have repeatedly come to light and a numberof papers have been retracted;• Some authors have tried to cheat journal editors by
supplying fake e-mail addresses for real scientists aspotential reviewers;• Unfounded historical revisionism and views based onsuch revisionism has appeared in descriptions of history in some school textbooks in Japan (and perhaps
in other countries as well);• Funding bodies require more and more short-term social “impact”;• Mass media pick up more and more sensational aspects of research with improper use of terms.
Together, these blur the distinction between scientific activities which are carried out in accordance with establishednorms of science and those activities that are not. Recall that science is destroyed when people, including “scientists,” start regarding claims and “arguments” based onforged or fake data as part of science.
In such a situation, automatic terminology processing may
contribute to the destruction of science through unconsciously extracting the abuse of concepts as normal andspreading them. Daille once argued for the necessity of detailed text profiling (Daille, 2008). If we start from corpora
or textual data, text profiling becomes more and more important. Theoretically, however, the relation between concepts (and terms) and texts is the other way round. Texts areconstructed in such a way that they make proper sense andconcepts and terms are assumed beforehand. Text profilingis concerned with providing machines with appropriate information while assuming that machines are disciplinised
rather than that they learn. Can we add the ingredient oflearning rather than only avoid inappropriate disciplinisation? What does this mean?
This is the situation which terminology processing currently is facing. Having this in mind, I introduce some practical terminological tasks and some trials. In fact, since themid-1990s, at the background of terminology processing,I have kept thinking of these issues. Words are grandiose,
deeds are miserably tiny. Worse still, the practical tasks introduced below are only remotely related to what we havediscussed so far. But let us move on anyway.

2. Issues in Terminology Processing
2.1. Terminology and Textual Corpora
Research in and the practice of terminology as an independent area of activity was first consolidated in Wüster’s
seminal work (Wüster, 1959), in which he put emphasis on
the rigidity of concepts and terms. Felber states that terminology starts with concepts rather than terms, is concernedwith the system of concepts in its synchronic state, and is
not concerned with the linguistic features of terms that are
unrelated to concepts (Felber, 1984).
In terminology, terms and concepts are defined as follows(de Besse et al., 1997):
term: A lexical unit consisting of one or more than one
word which represents a concept inside a domain.
concept: An abstract unit which consists of the characteristics of a number of concrete or abstract objects which
are selected according to specific scientific or conventional criteria appropriate for a domain.
Kageura showed that, theoretically, terminology as a coherent set of terms conceptually precedes individual terms;
terms are items within a terminology which in its totality reflects the conceptual system of a domain (Kageura, 2015).
Two features of concepts and terms can be pointed out here:
1. A concept represented by a term may be updated, butdoes not change through casual use. This update of theconcept is understood as a step towards the ideal stateof that concept, which exists as a regulatory ideal.
2. Terms and the concepts they represent are attributed tothe system of knowledge of the domain.
Since the 1990s, more descriptive approaches haveappeared (Budin and Oeser, 1995; Temmerman, 2000).
While these approaches have advanced how concepts andterms can be described, understanding of what concepts
are seems to have remained intact behind the scenes4 . The
Wüstarian view of terms has always been there as the regulatory ideal for terminology. This also holds for corpusbased automatic terminology processing. After all, without
this regulatory ideal, we do not need to and we cannot talkabout terms and terminologies as something different fromordinary words, compounds or collocations anymore.
In corpus-based terminology processing such as monolingual and bilingual automatic term extraction, this regulatory ideal that links the work to terminology is implicitly
taken into account when domain corpora are defined. Domain corpora are the discoursal part of the linguistic representation of the system of knowledge of the domain. This
discoursal part, to be relevant, makes use of the terminological part, which is the other part of the linguistic representation of the system of concepts and knowledge of thedomain. Though every now and then concepts are updated
through discourse, specialised discourse at the same time
critically depends on the system of concepts and the corresponding terminology.
4This perception may bring us back to Frege but we do notelaborate on this further here.

Thus term extraction thus should not be the task of extracting linguistic elements that are relevant to a given set oftexts or domain corpora; it is the task of extracting terminology that represent a system of concepts and thus the system of knowledge of the domain through domain corpora.
This contrasts with keyword extraction, which is definedas the task of extracting linguistic elements that are relevant to texts. One can extract keywords from a document
which consists only of fake information and the extracted
keywords can be valid, but one cannot extract terms fromsuch a document.
This is the theoretical reason why text profiling becomes critical in corpus-based terminology processing(Daille, 2008). The practical result that text profiling can
improve the performance of such tasks as bilingual term
extraction (Morin et al., 2010) can be a reflection of thistheoretical point. For text profiling, we can resort to external information at a variety of levels, such as the reliability of authors, of institutions authors are affiliated with,of journals, thus of publishers, or of the format of documents, etc. Unfortunately, it is not sufficient. We can see
this from the example we observed above, i.e. the planned
insertion of the statement “responsibility accompanies freedom” into the Japanese constitution. The agent trying to do
this is the governing party and once inserted the statement
will constitute a part of the Japanese constitution. In viewof the external criteria, this statement is to be regarded as“reliable,” even if it is nonsense. Ultimately, therefore, weneed to resort to knowledge itself to avoid this sort of misjudgement. But how? Note that here the problem has gone
beyond text profiling.

2.2. Conceptual Systems and Normativity
Two clues exist that guide us when dealing with this issue,
though neither of them provides us with direct solutions to
the problem we have discussed so far.
First, at a certain stage in the process of learning, humanbeings start judging information or a chunk of knowledge
that is given to them and start refusing to accept it. Thisis because they have nurtured their system of belief, which
is supported by the system of knowledge. One of the core
parts of this system of knowledge is a vocabulary, which is
not just a set of words but “a coherent, integrated systemof concepts” (Miller, 1986). In the arena of sciences, themost basic part of this system of knowledge is reflected interminology, which represents a coherent, integrated system of the concepts of the domain. A system of concepts
is not just a set of concepts randomly collected. It embodies normativity, to the extent that we can talk about degreeof systematicity and whether something is relevant to thesystem or not. Explicitly dealing with the terminology asa reflection of the system of concepts rather than dealing
with individual terms or a set of given terms, therefore, canbe a step towards properly handling terms, terminology andconcepts, i.e. dealing with terms consistently and systematically in such a way that they collectively reflects the meaningful part of the system of concepts of the domain.
Let me cite an example here, though it is not terminological. Suppose we are interested in extracting words fromtextual corpora to construct a dictionary. Suppose that weextracted a set of words from a corpus of 10,000 word tokens, and obtained two words that indicate types of fruit,i.e. “orange” and “apple”. From the point of view of constructing a dictionary, given the range of words referring tofruit that are used in daily life in many English-speaking
areas in the world, it is most natural that a dictionary which
includes “apple” and “orange” as entries would also have“banana” as an entry.
To obtain the word “banana” from the corpus, we may haveto extend the corpus to 100,000 word tokens. We would
then obtain “banana”, but would also obtain “mango” and“kiwi fruit”. We would most probably think that a dictionary that contains “mango” and “kiwi fruit” as entry words
should also have “papaya” as an entry. Otherwise, the setof entries lacks systematicity and coherency. To obtain “papaya”, we may have to extend the size of the corpus to, say,
1,000,000 word tokens. In addition to “papaya,” then, we
would obtain “kiwano” and “star fruit,” in which case wewould need “dragon fruit” to make the set of entries in the
dictionary coherent and systematic. This is the so-called“orange, apple, banana problem” 5 .
Although this description is imaginary, a situation equivalent to it can happen in real-world dictionary-making situations. Kilgarriff et al. (2014) found that, in a project that
aimed at developing monolingual and bilingual word lists
for language learning using corpora, for nine languages andthirty-six language pairs, it was preferable to define a set ofcommon key domains and populate the domains with words
independently for each language. As domains they definedcalendar, i.e. days of week, months, time, celebrations,colours, clothes, numbers, etc (Kilgarriff et al., 2014). Thisis partly because there is no guarantee that all the names
for the days of week exist in a given corpus. This also
indicates that the system of vocabulary or terminology
is not a secondary, artificial derivation while discourse
and texts are the first-hand manifestation of languages(Wilks et al., 1996).
The concept of normativity is also relevant to the textual
or discoursal sphere. For instance, we do not refer to the
New York Times, let alone AmericanNews.com, to makea legally learned argument about the Indonesian invasionof East Timor in 19756 . We refer to binding internationallaw and authentic political records7 . Indeed, researchers,
irrespective of their research area, should be fully aware ofwhat is called normativity here; they refer mostly to peerreviewed and other academically reliable papers. They do
not regard these papers and anonymous blog posts as equiv5

Personal e-mail communication with the late Dr. Adam Kilgarriff on April 1, 2014. Though the conversation took place on
April Fool’s Day, the content was academic.
6
The Indonesian invasion of East Timor began on 7 December
1975, one day after then U.S. Secretary of State Henry Kissinger
left Jakarta.
7
The National Security Archive of George Washington University revealed the conversation between then U.S. President Gerard R. Ford and Kissinger and then Indonesian president Suharto, responsible for the invasion and the massacre that followed.
The record showed that U.S. hadgiven “greenlight” to Suharuto’s planned invasion.
See
https://nsarchive2.gwu.edu/NSAEBB/NSAEBB62/.

alent. This observation is closely related to the proposal oftext profiling mentioned above.
Documents thus have a degree of normativity. This concept is also frequently valid within cross-lingual setups. We
often observe that a bilateral contract made between institutions in different countries in two languages adds such astatement as “in case of discrepancies between the versions
in two languages, a preference is given for the interpretation according to a version in which the contract was originally drawn up.” In bilingual or multilingual situations, it
is usually the case that the document is written in one language, which is the source language. The corresponding
documents in other languages are created through translation. This implies that, not infrequently, when context
vectors for corresponding terms in different languages havesome gaps, they are not relative to each other. We sometimes need talk about deviations of the usage of a term or
the concept represented by a term, as in the case of the definition of cancer in insurance policies.
Linguists may say that normativity of terms and documents
is not inherent in languages. May be true, but terms andterminologies are the functional class of languages and thedetermining factor is social and/or conceptual, which are
not linguistic in the first place. What we see is that linguistics in its narrower sense falls short of addressing the issue
we have observed so far. I see no merit to sticking to thepurity of linguistics or whatever that cannot counter the destruction of the very conditions which enable us to sensibly
communicate with each other, without resorting to physicalviolence. Freedom, in its essence, is never accompanied byresponsibility, even if 99 percent of people claim that thisis so. The rights of individuals, in their essence, are neveraccompanied by duties, even if the governing party of a nation declares this to be so. The concepts of freedom andrights should be properly maintained, logically, even when
oppressive and discriminative discourse becomes prevalent.

3. Directions in Terminological Research
We can define a range of terminological studies andterminology-processing tasks that take into account theconcepts of normativity and/or systematicity, both in monolingual and in bilingual or multilingual situations. To do so,
we can conveniently distinguish two phases of terms andterminology: individual terms and concepts they represent
as they are and in their use in texts, and the system of terminology and conceptual system.

3.1. Terms, Concepts and Use of Terms
If we focus on individual terms, their relation with concepts
is the point of central importance, as has been pointed out
by theoretical terminologists. Terminologists pay great attention to how to define concepts properly. Note that terminological lexicons with proper entries and reliable definitions are used as a resource that people commonly refer toand attain normative status. Normativity inevitablly accompanies tasks dealing with concepts represented by terms.
Referring to terminologists and other specialists activities,
we can define, for instance, the following tasks as taking
into account the issues we raised in the previous section:

Creating/extracting definitions: We can define a task ofcreating or extracting a normative definition(s) for a given
term using corpora or other resources. In its ordinary
sense, definition extraction is a well-established NLP task
(Sierra et al., 2009). We can also regard word embeddings
as the task of defining word meanings.
Detecting deviations: In the context of what we have discussed so far, what matters about definitions is their normativity. So one possible application – or evaluation schemeof definition extraction through application – can be thetask of detecting deviated use of terms in terms of theirdefinitions. Automatically judging that the statement “responsibility accompanies freedom” is misusing the concept of freedom gives a concrete image of the objectiveof this task. It is somewhat similar to word sense disambiguation and also outlier detections used for evaluating
word embeddings (Camacho-Collados and Navigli, 2016),
though these tasks regard meanings as relative. At a different level, this task is related to detecting logically inappropriate statement. We started a research for detecting deviations of usage of technical terms in Japanesemass media, currently focussing on the domain of law andpolitics. A very embryonic observation was reported in(Tang and Kageura, 2017). When term variations exist, i.e.
different representational forms are regarded as representing the same concept (Daille, 2017), controlling the surface
form of terms also becomes an issue accompanying deviation detection.
Detecting cross-lingual gaps: As in the case of “cancer”
and its Japanese “equivalent,” terms in different languages
that are regarded as representing the same concept can bedifferent in some critical details. Not only judging the degree of correspondence but also evaluating the critical difference will be an important task as an extension of bilingual term extraction from parallel or comparable corpora.
If we take into account the fact that very frequently corresponding documents in two or more languages do nothave the same status (the goodness of TL texts should be
evaluated by using original SL texts as the norm), the task
is defined as directed, using the normative concepts represented by SL terms to judge the concepts (also normativein a monolingual setup) represented by TL terms8 . At adifferent level, dealing with representational variations also
becomes an issue.
Text profiling: Social profiling of texts can provide corpusbased processing with external criteria of normativity. To
what extent texts themselves can be used to evaluate theirnormativity is also an important issue. This is technicallyrelated to text clustering or classification.
As technical problems involved in these tasks are similar to
related tasks that have been well established, methods proposed for these related tasks may be adopted to tackle these
problems. The difference resides in definitions of problems.
8

One may argue that MT deals with this issue indirectly when
TL expressions are selected. For professional translators, being
able to explain the difference among possible choices of TL expressions and the reason why a particular expression was chosen is
not only part of their competence but also part of the end-product;
the definitions of end-to-end in MT and in human translation are
different.

Note that issues related to above topics are recently being
dealt with in NLP. For instance, fact checking and analysing
and detecting biased language are listed as topics relevantto the Workshop “Natural Language Processing meets Journalism.” In the field of terminology, most of these havebeen dealt with manually.

3.2. Terminologies and System of Concepts
While we have witnessed a great advance in methods ofboth monolingual and bilingual automatic term extraction(ATE), the systematicity or coherency of extracted terms
have not been taken into account when these methods were
evaluated. Indeed, it is not stated as one of the aims of
ATE in most cases. It is understandable, as we do not really know how to measure systematicity or coherence ofterminologies. In lexicography, selecting a coherent set ofheadwords for a dictionary is left to the expertise of experienced lexicographers and remains one of the last frontiersof lexicography yet to be systematised9 . Nevertheless, as
we discussed above, systematicity is one of the essential
features in knowledge and thus to address this issue is critical to the study of terminology.
Taking into account that terminologies represent conceptual
systems, we can define, among others, the following tasks:
Evaluating systematicity of terminologies in terms ofconceptual systems they represent: Terms are relatively
motivated. Complex terms, which constitute 70 to 85 percent of all the terms in most domains in many languages,
represent concepts by showing their main conceptual characteristics and their relations through constituent elements(Sager, 1990). A terminological representation thus reflects
conceptual system to a certain extent. How systematic aterminology represents the corresponding conceptual system depends on domains and languages. Here we candefine the issue of systematicity of terminological representations vis-à-vis the conceptual system. Once we canestablish a method that can evaluate the systematicity ofterminological-conceptual system, we may be able to judge
to what extent a newly obtained term is relevant to theconceptual system and thus to the domain. The dynamic
modelling of terminological and conceptual growth can beconsidered as an extension of this task. Ontology building
shares a similar concern, though it focuses on conceptual
system rather than representations.
Evaluating cross-lingual differences in the systematicity of terminologies: Terminologies of different languages
represent the same conceptual system10 differently. Evaluating the difference in the systematicity of terminologicalrepresentations in different languages not only is important
for the theoretical terminology but also contributes to crosslingual terminological applications.
These studies are important not only from the theoreticalpoint of view but also for real-world applications. Recall
9Personal communication with Dr. Judy Pearsall of the Oxford
University Press on 8 July 2010 at the occasion of Euralex 2010
held in Leeuwarden, The Netherlands.
10
Though the case of “cancer” indicates that it is not necessary
safe to assume the identity of conceptual systems represented incorresponding terminologies in different languages, we can assume that they are approximately the same.

that in the keynote presentation in BUCC 2017, Professor Philippe Langlais stated “Despite numerous studies devoted to mining parallel material from bilingual data, wehave yet to see the resulting technologies wholeheartedly
adopted by professional translators and terminologists alike(Langlais, 2017). One of the reasons that not many advanced term extraction methods are not used in the realworld terminology management tasks or in translation isthat it is difficult to know what are extracted and what are
missed. If one could judge the status of extracted terms in
relation to the existing set of terms, terminologists would be
able to take advantage of the results of advanced methods
more comfortably. The problem of dealing with the systematicity of terminologies within data-oriented or corpusbased language processing framework is that the size of terminologies are small. This may be one of the reasons why
not much work has been carried out that deals with terminologies per se11 .

4. Two Concrete Studies

graphs are created for terms in these subclusters, andnew term candidates are generated by interpolating themissing links.
4. Bilingual candidates are generated by compositional
matching, assuming that terms are motivated roughly
in the same manner.
5. Candidates are validated by Web search.

Figure 1: An exemplar terminological network.

We introduce here two concrete studies we have been andare carrying out, which (remotely) take into account the issues of systematicity and normativity of terminologies andterms. The first is augmentation of bilingual terminologies,
and the other is controlling term translations.

4.1. Augmentation of Bilingual Terminologies
In some languages pairs, as in the case of Japanese andEnglish, manually constructed high-quality bilingual terminologies exist, and there is a strong demand for updating these terminologies. Standard corpus-based bilingual term extraction, unfortunately, cannot satisfy this demand, because new terms mostly occur with low frequency in the corpus and often hard to extract, and therelationship between extracted terms and entries in theexisting terminologies is not transparent. Against thisbackdrop and taking into account issues we have discussed so far, we are developing a terminology-driven
method for augmenting existing bilingual terminologies(Iwai et al., 2016a; Iwai et al., 2016b). The framework is
simple:Figure 2: Subclusters in computer science (top) and economy (bottom) in Englih (left) and Japanese (right).

As of now, the method has several shortcomings:

1. Assuming that terminologies systematically reflects
conceptual systems, we define terminological network
which represents termino-conceptual structure of thedomain with terms as vertices and edges as common
constituent elements among terms. Figure 1 shows aterminology network of a small putative terminology.

1. The degrees of systematicity at the representational
level vis-à-vis the conceptual systems have not beentaken into account. The method just assumed thatterms are reasonablly well motivated and thus terminologies systematically reflect the underlying conceptual systems to a degree that we can simply use terminological representations as a key to approximate conceptual systems. This assumption holds in monolingual situation, but as shown in Figures 1 and 2, different languages represent different parts of conceptual
systems.

2. Apply partitive clustering to the terminological network to obtain subclusters of terms which represent
conceptual subsystem (Figure 2). Corresponding terminologies in different languages show similar tendencies, though differences are not small.

2. The gap between the systematicity of English andJapanese terminologies as reflected in the terminological networks can be explored to further capture the terminological structures that reflect conceptual systems.
We have not elaborated on this.

3. Complex terms are formed in accordance with the dynamics of these subclusters. Head-modifier bipartite

3. Terminology networks are defined in a very rudimentary way. As edges are made when two terms (vertices) have common constituents, the hierarchical relations encoded in the forms of terms are not reflected in
the network. Also, the dependency relations between
constituent elements within terms are not encoded in
the networks. This is the other side of the fact thatthe method currently does not take into account the11Some other reasons are: terminologies and dictionaries are
generally regarded as secondary creations compared to documents, which is based on the misunderstanding of languages; andterminologies are not too large and stable so it has been held thatmanual handling suits better than automatic processing.

conceptual system (see 1 above) and carries out candidate term generation purely at the level of terminological representations. It would be more theoreticallyproper to define the conceptual system separately fromterminological networks, make correspondences between these two layers, and resort to the information atthese two levels. To define the conceptual system that
corresponds to a given set of terms, we are currently
examining the use of distributional representation of
constituent elements of terms in terminologies.
4. Currently all the candidates are treated equally. Using the information contained in termino-conceptual
structure, we can give weight to candidates in terms of
their status within the termino-conceptual system.
We are currently working to overcome these issues.

4.2. Controlling Term Translations
In translating terms, one TL term for an SL term isthe basic principle for properly controlled documents(Sharoff and Hartley, 2012). In practice, it is frequently thecase that several different TL terms are used for a single
SL term. Terminology control should be made at the earlystage of translation projects, i.e. controlled bilingual terminologies should be provided with translators involved in
the project before they start translating documents. Whilelanguage service providers generally adopt this procedure,
it is still difficult to control terms properly. For instance,
across Japanese municipalities, Japanese terms for administrative procedures are the same, but their translations varybecause each municipalities translate their documents independently to each other. In these cases, “posterior” terminology control is essential; it is posterior in relation toalready translated documents, but prior in relation to future
documents to be written and translated.
One of the theoretically essential and practically important
issues is to estimate the coverage of collected terms12 . Thisissue is related to several other questions, i.e. whether or
not the size of the corpus should be extended to collect sufficient number of terms, how many more texts should bechecked, and how controlling terms affect these tasks.
We carried out TL term control for Japanese municipality
documents manually (Miyata and Kageura, 2018). We collected three Japanese-English parallel documents that describes municipal procedures and extracted bilingual terms
from them. Table 1 shows the number of terms (V (N ) indicates the number of term types, N the number of term
tokens). Although we collected corresponding terms fromparallel documents, the number of terms both in types andin tokens differ between two languages. We identified 374
Japanese term variations (12.4%) and 1258 English term
variations (36.3%); TL terms have more variations than SL
terms (Warburton, 2015).
Variations were groped and a preferred term for eachgroup was assigned, based on three types of evidence, i.e.
frequency evidence, topological evidence (expressions ofterms) and dictionary evidence. After the terminology con12

That extracted terms be evaluated in terms of coverage is aprerequisite for evaluating systematicity of terminologies.

Japanese
EnglishV (N )
3012
3465

N
15313
15708

N/V (N )5.08
4.53

Table 1: The number of extracted terms

trol, the numbers of term types in Japanese and English became closer.
Japanese
EnglishVc (N )2802
2740

V (N )c /V (N )
93.0%
79.1%

N/Vc (N )
5.47
5.73

Table 2: The number of extracted terms
What do they mean for the status of terms we collected?
First, to evaluate the status of terms we collected vis-à-vis
potential terminology we are dealing with in the domain,
we adopted self-referring evaluation of collected terms.
The idea is simple: (a) estimate the population number ofterms using the distributional information of the terms wecollected, and (b) evaluate the coverage of the collected
terms against the estimated size of terminology. For theestimation of population number of terms, we used LNRE
models (Baayen, 2001; Evert and Baroni, 2007).

Figure 3: Growth rate of terms to the corpus size.

Figure 3 shows the growth rate of terms, before and afterterminology controll was applied. We can observe several points: coverage became higher when terms were controlled and if we extend the corpus size to 40,000 word tokens, only one out of 40 terms is expected to be new. These
enable us to evaluate the status of terms and terminology
controll and ROI-based evaluation of the usefullness of extending the corpus.

5. Conclusions
We examined theoretical and social issues related to terminology, and clarified the position of terms and terminologies in relation to textual corpora together with issues in
corpus-based terminology processing. We argued that theidentity of concepts represented by terms is supported by
the regulatory ideal, which provides the conditions uponwhich we can rationally communicate with each other inthe first place. The concepts of systematicity and normativity were then introduced as on-the-ground concepts thatreflect the regulatory ideal of the identity of concepts. We
defined a range of tasks that take into account these issues
and introduced two concrete studies as examples.

Much of what we discussed here is yet to be fully pursued,
although relevant technologies exist. Indeed, the same technologies that can be used to pursue the tasks defined here
can easily be used to promote pseudo-communication, including ”fake news and other forms of communication that
promote hatred and discrimination. Unfortunately, current
data-driven ML technologies do not internalise the regulatory ideal that human beings have tried to pursue painstakingly, so it is still upon us to decide how these advanced
technologies are used. Cross-lingual comparable corpora
contain interesting and important gaps, which we can explore to promote mutual understanding, as understanding
starts from the recognition and identification of differences.

6. Acknowledgements
I would like to thank the organisers of BUCC Workshop,
Professor Reinhard Rapp, Professor Pierre Zweigenbaumand Professor Serge Sharoff for giving me an opportunity togive this talk. Terminology-driven augmentation is a joint
work with Dr. Koichi Takeuchi and Mr. Kazuya Ishibashi of
Okayama University and Ms. Miki Iwai and Mr. Long-Huei
Chen of the University of Tokyo, and was partly supported
by JSPS Research Grants 24650122 (2012–2014). Evaluation of controlled terminology is a joint work with Dr. Rei
Miyata of Nagoya University, and was partly supported by
JSPS Research Grants 25240051 (2013–2017) and KDDI
Foundation Research Grant Program (2014–2016).

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