Closing the Gap between Corpora and Termbases, CHAT2013

Developing effective terminological
resources for commercial use
or
Narrowing the gap between
termbases and corpora
in commercial environments

Kara Warburton
CHAT 2013

© 2013 by Termologic. All rights reserved.

1

1. Motivation of my research
2. Does commercial language contain terminology?
3. What is a term?
4. Our assumption about termbases and corpora
5. Aim and methodology of our research
6. Corpus-valid terms
7. The gap between termbases and corpora
8. Causes of the gap
9. Keywords and their potential
10.Conclusions

2

Personal motivation
●

The established principles and methodologies of
terminology management don't seem to “fit” the needs of
commercial uses of terminology

How do I resolve this apparent conflict?
●

●

Study how terminology is managed in commercial settings
Identify key issues, gaps with mainstream methodology
and theory


3

Mainstream theory and
practice

Commercial needs

Strict ties to translation
Restricted focus of termbases
Normative
Onomasiological
Thematic
Univocal
Objectivist, concept focus
Philosophical, social concern

Polyvalent


Prescriptive and descriptive
Largely semasiological
Ad-hoc
Multivocal
Communicative, language focus
Commercial concern

4

What is terminology?
●

●

●

●

(Terminology is) the science studying the structure,
formation, development, usage and management of
terminologies in various subject fields
(A terminology is a) set of designations belonging to one
special language.
(A special language is) a language used in a subject field
and characterized by the use of specific linguistic means of
expression.
(A subject field is) a field of special knowledge.

(ISO 1087-1, 2000)

5

According to these definitions
●

An LSP (special language) contains terminology.

●

Key criteria for LSP:
–
–

●

Subject field
Specific linguistic means of expression

Therefore:
–

If commercial language is an LSP, then it contains
terminology.

–

Commercial language is an LSP if it:
●
●

can be viewed as a type of subject field
has specific linguistic means of expression


6

What is a subject field?
What is “special” knowledge?
●

●

●

Pure and applied sciences, techniques, technologies,
specialized activities
Professional activities carried out in business, industry,
companies, and professional settings
Any specialized activity carried out by humans


7

What are “specific linguistic means of expression”?
●

Textual characteristics
–

concision, precision, depersonalisation, economy,
referentiality, preponderance of nominal structures,
dominance of written form

●

Communicative situation: formal, professional

●

Communicative purpose
–
–

●

inform, educate
objective, precise, concise, and unambiguous exchange of
information

Conscious acquisition


8

Commercial language is an LSP
●

●

Describes tangible products, services, and activities, often
within one vertical industrial or economic sector, which
could be viewed as a subject field
Adheres to specific linguistic rules and styles; many
companies have a style guide, some are automatically
implementing the style rules through controlled authoring
software

●

Written form predominates

●

Informative purpose


9

What is a term?
●

●

●

●

●

General theory: the designation of an object the conceptualization of which can be classified into a system of concepts
Socio-cognitive theory: a natural language representation of a
unit of understanding, considered relevant to given purposes,
applications, or groups of users
Lexico-semantic theory: a construct that takes shape through an
analysis which gives consideration to corpus evidence, subjectmatter relevance, and the purpose of the terminographical
product
Textual theory: a semantically-charged linear structure that
contributes to texture (coherence and cohesion) in an LSP text
Communicative theory: all the above

10

What is a term for commercial terminography?
●

●

●

●

Semantic membership in a subject field is a guiding
criterion
But bringing benefit to the company is the primary criterion
Companies have diverse needs, requiring diverse types of
terminological resources
......


11

Applications of terminology
●

computer-assisted translation

●

controlled authoring

●

content management, automatic content classification

●

product classification

●

indexing, SEO, keyword management, etc....

EACH of these applications requires a HIGH LEVEL of
correspondence between the termbase and the company
corpus.

12

A term is...
●

ANY lexical unit that can bring benefit to the company by
being “managed” is a candidate “term”. This MAY include:
–

General lexicon words

–

Phrases

–

TM segments

–

Proper nouns

–

Variants

–

Non-nouns, especially verbs


13

Aim of our research
●

Compare termbases and corpora in four IT
companies to see how well they (the terms)
correspond

●

Establish the scope of the gap

●

Explain the gap

●

Identify ways to reduce the gap


14

Methodology of the research
●

Obtain termbases in export files from 4 different systems

●

Convert to TBX

●

Import to TermWeb

●

Apply necessary filters for different evaluations

●

Obtain and prepare corpora for analysis

●

Export corpus-valid terms from termbase

●

Run batch concordance of termbase terms

●

Statistically analyze results

●

Identify patterns, investigate solutions, including keywords
and DICE ranking


15

Profile of the companies
●

HQ in USA but global presence, all in IT sector

●

Company A
–
–

330 employees

–

●

Field: statistics
Across language server, CrossDesk, CrossAuthor, xMetal,
CrossTerm

Company B
–

Field: business analytics

–

13,000 employees

–

Acrolinx IQ,in-house CAT tools, TermWeb


16

Profile of the companies
●

Company C
–
–

18,500 employees

–
●

Field: information security, storage, management
SDL WorldServer, Acrolinx IQ

Company D
–

Field: hardware (PCs, servers, printers, networking),
software

–

330,000 employees

–

SDL Trados and MultiTerm


17

Size of data
Corpus
size in
tokens

Terms from
termbase

Size of
corpus in
relation to
termbase

1

3,973,265

1,777

2,236

2

19,808,928

6,441

3,075

3

22,136,564

4,195

3,074

4

400,777

4,385

91


18

The gap between termbases and corpora

Range 0 + A:

Company A
Company B
Company C
Company D


35%
63%
73%
76%

19

Causes of the gap
A) Under-performing termbase terms
–

termbase terms that are absent or are infrequent in the
corpus (generally, redundant terms)

B) Under-documented corpus terms
–

corpus terms that are either entirely missing from the
termbase (nonextant terms) or are in insufficient
number in the termbase (infrequent terms).


20

Under-performing termbase terms
●

Upper-case terms

●

Excessively long terms

●

TM segments

●

●

Terms with unessential modifiers (boundary setting
problem)
Terms with proper name modifiers


21

Term boundary problems
Nonextant term
bad cluster
automatic incremental backup

Adjusted term
cluster
incremental backup

sequential mean squares
absolute correlation coefficient
individual fitted values
active data source
critical success factor component
printhead failure

mean squares
correlation coefficient
fitted value
data source
critical success factor
printhead


Frequency
8,490
521
129
330
270
7,201
540
275

22

The cost of redundant terms
●

●

●

IT cost
Reduced efficiency due to dilution of “good”
entries
Cost of creating and maintaining the entries


23

Under-documented corpus terms
●

Variants

●

Non-nouns (particularly verbs)

●

Homographs

●

Terms with optimally-set boundaries

●

Multi-word terms containing a keyword

●

Adjectives that are productive in forming MWTs


24

Keywords
●

●

●

A word that is unusually frequent, therefore, likely a
domain-specific unigram term
Determined by comparing word-frequency lists from a
domain corpus and a general purpose (reference) corpus
Good indicators of the key topics of a corpus


25

Keyword categorization
●

High-ranking - highly domain-specific
–

●

Mid- and Low-ranking - potential for domain-specific
homographs
–

●

data, plot, syntax, command, string, server

worm, cloud, wizard, key, boot

Keywords that are absent from or are extremely rare in the
reference corpus - less frequent but also highly domainspecific
–

dotplot, ODBC, toolbar, widget, spyware, phishing


26

Keywords as nodes of MWTs
●

●

●

Highly productive in
forming domain-specific
multi-word terms (MWTs)
Have been successfully
leveraged in term
extraction research
Successful search
techniques include raw
collocate frequency and
DICE collocate relationship
measure


27

Verbs
assess
overlay
remove
replicate
save
fail
choose
enter

display
customize
gage
delete
calculate
censor
edit
forecast
plot


specify
select
create
click
plot
display
access
test

run
return
enable
update
compute
tick
delete
design

28

Key findings
●

Unigrams and bigrams make up the vast majority of
termbase terms that occur frequently in the corpus.

●

Terms that present the situation of homonymy are
important to document in a termbase


29

Key findings
●

Verbs and adjectives are under-documented in termbases

●

Termbases are underoptimized when it comes to
documenting frequent domain-specific terms. Only three to
eight percent of the termbase terms occur very frequently,
and only 13 to 17 percent of their termbase terms occur
frequently. Only one company managed to include a
moderate level of frequent terms in its termbase (37
percent).

30

Conclusion
●

For commercial terminography, the notion of termhood
needs to take into account not only the traditional semantic
criteria but also pragmatic and purpose-driven criteria.

●

Terminography serving commercial purposes needs to be
more corpus-driven.

●

This is not “terminology” in the traditional sense. It includes
various types of lexical resources.


31

Backup slides


32

In-house pracices
●

●

●

●

●

All 4 companies are interested in using terms in controlled
authoring (CA); 3 companies are doing so already.
Only one company (A) maintains all its data in a single
termbase. The other companies maintain separate
termbases for various purposes, such as CA and CAT.
Company D has 15 termbases.
Company B maintains 3 separate termbases: CA, CAT,
and Authoring/Publishing aid.
Company C uses automatic term extraction.
Company D imports TM segments into the termbase to
compensate for technical limitations of TM matching.

33

Most common termbase data categories
●

Definition

●

Part of speech

●

Process status

●

Usage status

●

Term type


34

Most common termbase problems
●

●

●

None of the companies use subject fields
Only 2 companies consistently mark the part-ofspeech
There are widespread violations of:
–

Term autonomy

–

Concept orientation

–

Data elementarity/granularity


35

Corpus-valid termbase terms
●

●

●

●

●

A notion defined strictly for the purposes of our research
Terms that we “count” to measure of the gap between the
termbase and the corpus
Terms in the termbase that can reasonably be expected to
occur in the corpus
Does not include terms with negative usage markers (do
not use, deprecated, etc.)
Does not include general lexicon words, due to application
specificity for controlled authoring, reduced terminological
“interest”, and high expected number of concordances

36

Example of a filter for corpus-valid terms


37

●

Termbase verbs occur 26,000 times

●

Keyword verbs occur 90,000 times


38

Closing the Gap between Corpora and Termbases, CHAT2013

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