17. Anne Schuman (USAAR) Terminology and Ontologies 2

Terminology and Ontologies
Section 2: Current Research Topics
Anne-Kathrin Schumann
Saarland University
“Expert“ Winter School
Birmingham
November 13, 2013

Overview

 Current trends in research
 Term variation
 Culture-specific semantic differences
 Definitions, contexts, knowledge-rich
contexts
 Usability aspects

 Term extraction and term mapping

Current trends in research
 Controversial paper by Cabré in Terminology 5 (1),
1998/1999, pp. 5-19: Do we need an autonomous theory
of terms?

“It is increasingly being accepted that Wüster‘s
theoretical stance […] is proving inadequate for the
different current needs of term description and
processing because of its idealising and simplifying
approach.“
(markup is mine)


 What have we been talking about?
 terminology adopts a decompositional, structuralist approach to
the description of specialised meanings
 the meaning of a terminological unit (concept+term) can be
described by a set of sufficient and necessary semantic invariants
 no interest in the linguistic domain of the field:
“Only the designations of the concepts, the lexicon, are relevant to
the terminologist. Syntax and inflection are not. For the latter, the
same rules apply as in general language .“
(my translation from Wüster 1985: 2, markup as in the original)


 Terminology, then, is an exercise of reducing the complexity of
reality to simpler feature structures

“[D]iscreteness is in the head and fuzzyness is in the world.“
(Geeraerts 2010: 132)

 Main criticism: No account for
 the multidisciplinary (denominative, cognitive and
functional) nature of terms
 the communicative dimension of terminology
 connotational aspects in terminology
 the linguistic dependence of terms on particular languages
 pragmatic/functional aspects of term variation

 Small recap: term variation





is ubiquitous
is a problem for applications that use terminology
Wüster‘s solution: standardisation
counter-proposal: systematic study and handling of term
variation


Da jedoch der Massenstrom gleich bleiben muss, weitet sich bei einer frei
angeströmten Windkraftanlage der Wind auf, da eben trotz der geringeren
Geschwindigkeit hinter der Anlage die gleiche Menge Luft abtransportiert werden
muss. Aus eben diesem Grund ist die komplette Umwandlung der Windenergie in
Rotationsenergie mit einer Windkraftanlage nicht möglich: Dafür müssten die
Luftmassen hinter der Windkraftanlage ruhen, könnten also nicht abtransportiert
werden.
(Wikipedia)
-> coreference chains for text cohesion

 Term variation:
 cannot be treated only prescriptively because it is
functional from a linguistic point of view
 terms are reiterated in discourse for reasons of cohesion
 the informativity of the term is managed by altering the
form of the term (especially if it is a MWT)
 the whole form can normally be retrieved from context
(Collet 2004: 102)
-> term variation is influenced by text-linguistic aspects

 Other reasons for terminological variation:







dialects and geographical variation
chronological variation
social variation (e.g. academic expert vs. practitioner)
creativity, emphasis, expressiveness
language contact
conceptual imprecision, ideological reasons (e.g. “armchair
linguistics“) and different points of view (ozone layer depletion,
ozone layer destruction, ozone layer loss, ozone layer reduction)

(Freixa 2006)

 What is a term variant?

“ … an utterance which is semantically and conceptually related to
an original term.“
(Daille et al. 1996: 201)
-> an attested form found in a text
-> there is a codified (authorised) original term
-> semantically and conceptually related

 Types of variants:

 graphical: missing hyphen (e.g. Windkraftanlage vs.
Windkraft-Anlage) or case differences
 inflectional: orthographic (e.g. conservation de produit vs.
conservation de produits)
 shallow syntactic:
 variation of preposition (e.g. chromatographie sur/en
colonne)
 optional characters (e. g. fixation de l‘azote vs. fixation
d‘azote)
 predicative use of the adjective

 syntactic:
 additional modifier
 additional nominal modifier (closed list, e.g. protéine
végétale vs. protéine d‘origine végétale)
 expansion of the nominal head
 permutations (e.g. air pressure vs. pressure of the air)

 morphosyntactic:
 alternation between preposition/prefix (e.g. pourissment
aprés récolte vs. pourissment post-récolte)
 derivations (e.g. acidité du sang vs. acidité sanguine)

 paradigmatic substitution (e. g. Ehemann vs. Ehegatte)
 anaphoric uses
 acronyms

(Daille 2005)

 Variant recognition given a set of candidate terms:
 string similarity for inflectional/orthographical variants
(candidates with same POS shape and same length):

 rule-based correction of lemmatisation errors

 Variant recognition given a set of candidate terms:
 term variation patterns for rule-based variant
recognition

(Weller et al. 2011)

 Terminology considers specialised concepts to be
universal across languages
 For general language, this view is outdated (pragmatics,
text linguistics, cultural differences etc.)
 But also for LSP, things are not that easy

 Schmitt (1999) mentions different types of semantic
differences on the CONCEPTUAL level, e.g.
 culture-dependent differences between conceptual
hierarchies
 culture-dependent semantic prototypes

 culture-dependent differences between conceptual
hierarchies
 e.g. different concept systems for steel in Germany and the
USA
“Primary coolant system interconnecting piping is carbon steel
with internal austenitic stainless steel weld deposit cladding.“

carbon steel = Kohlenstoffstahl?


carbon steel = Baustahl
(+ term variation …)
“Most dictionaries fail to provide
accurate descriptions, especially in
problematic cases …“
(Schmitt 1999: 219, my translation from
German)

• typical “German“ hammer:
nr. 1 (second from left)
• typical hammer in UK and
US: nr. 4 (first from right)
-> complicated translation
strategies, e. g.
• insertion of a functional
equivalent
• insertion of semantic markup (“In the US, the hammer
typically used is the …“)
• adaptation of drawings etc.



“Apply the parking brake firmly. Shift the automatic transaxle to
Park (or manual transaxle to Neutral).“
->
„Handbremse fest anziehen. Schalthebel in Leerlaufstellung
bringen (bei Automatikgetriebe Wählhebel in Stellung P bringen).“
(Schmitt 1999: 255)

 Intermediate summary

 Translation is a knowledge-based activity involving deep
semantic analysis, functional adaptation and the creation of
discoursive cohesion.
 These issues affect terminological choices.
 Detailed terminological descriptions are needed



 to cope with lexical issues (term variation),
 to constrain terminological (semantic) and, consequently,
translational choices.
The quality of a translation is a matter of functional adequacy (usability
in the target system and language and the intended context) rather
than linguistic (surface or structural) or even semantic similarity (skopos
theory).

 Intermediate summary: some research questions

 How to improve (or adapt) NLP techniques (lemmatisation,
spelling correction/variant detection, compound splitting) for
specialised domains?
 How can we identify term variants and map them to their
“canonical“ counterparts?
 Can we use term variants for making (automatic) translation
or any other NLP task more fluent?
 To which degree are variants detected by TM systems and can
we improve on that?
 How can we provide richer semantic descriptions for terms?

 Definitions, contexts, knowledge-rich contexts

(ISOCat)

 Definitions are traditional parts of lexicographic entries
and were “inherited“ by terminology (but few resources
really provide them).
 There are different kinds of definitions and different
ways of using them.
 Lexicographic definitions explain lexical meanings
whereas terminographic definitions describe concepts.
 Terminography normally requires richer descriptions
than standard definitions.

 Examples of lexicographic definitions

Linguistics: The scientific study of language
Categorical: Of or belonging to the categories.

- Usually not a complete sentence
- Often only with reduced information (certainly not enough
for learning the concept)
- Direct reference to specific lexical units

 Terminological definitions
Definition types
relate the concept to its hypernym (class of
objects, “genus proximum“)

enumerate all objects that fall under the category
in question

state how it differs from other hyponyms of the
genus proximum (“differentia specifica“) ,
„intension“ of the concept

“extension“ of the concept, “extensional“
definition, Wüster: “Umfangsdefinition“

A definition which describes the intension of a
concept by stating the superordinate concept and
the delimiting characteristics. (ISO 12620, ISOCat)

A description of a concept by enumerating all of
its subordinate concepts under one criterion of
subdivison. (ISO 12620, ISOCat)

 Terminological definitions
 Examples

“The planets of the solar system are Mercury, Venus, Earth, Mars, Jupiter,
Saturn, Uranus, Neptune and Pluto.“
(Bessé: „Terminological Definitions“. In Wright/Budin 1997, pp. 63-74)
„Defektivum. Wort, das im Vergleich zu anderen Vertretern seiner Klasse
‚defekt‘ ist in bezug (sic!) auf seine grammatische Verwendung, z. B. bestimmte
Adjektive wie hiesig, dortig, mutmaßlich, die nur attributiv verwendet werden
können.“
(Bußmann: Lexikon der Sprachwissenschaft)

 Many other classifications, see e.g. Cramer 2011

 Context

 Standard category in terminological entries
 Important, but under-specified
 Context as usage example, e. g. „Photosynthesis takes place primarily in
plant leaves, and little to none occurs in stems, etc.”
-> can provide linguistic information (selectional preferences,
collocates)
 Context as semantic description, e. g. „The parts of a typical leaf include
the upper and lower epidermis, the mesophyll, the vascular bundle(s)
(veins), and the stomates.”
-> provide semantic information, including information about conceptual
relations
(examples from IATE)


 Knowledge-rich contexts (KRCs, e.g. Meyer 2001)

 My take on KRCs
 Sentences that provide relevant bits and pieces of information (subject to
the definition of relevant semantic relations) that, taken together, can be
used for building rich semantic descriptions.
 (Intentional or extensional) definitions are subtypes of KRCs.
 There is much more information in texts than just restircted types
definitions.
 Annotating KRCs in corpora is hard
 Which is the domain?
 Which is the definiendum?
 Which semantic relations are relevant for (generic or domain-specific)
terminological descriptions?
 Annotators prefer Aristotelian statements and are biased by lack or existence of
domain knowledge (Cramer 2011, Schumann 2013).
 Research results for different languages mentioned in references section

 Usability aspects
 How to support terminological workflows?
 For which groups of language workers is terminology
relevant?
 What kind of information do they look for?
 Which kinds of software and formats do they use?
 Survey (1782 respondents) conducted within the TAAS
project (http://www.taas-project.eu/)
 information and graphics provided by KD Schmitz

 Intermediate summary
 The needs of language workers are rather clear (tools, data
formats, time constraints, information needs, …).
 Rich terminological descriptions are needed.
 Semantic (conceptual) information seems to be more
important than linguistic information (score Wüster^^).
 However, some linguistic issues need to be handled.
 Almost all terminological resources are deficient in the most
important types of information (semantic information).

Term extraction and term mapping

 Term extraction
 Standard approach (for European languages)
 POS filtering
 Statistical filtering against a reference corpus
 (filtering against stop list, frequency threshold)


 Term extraction
 Statistical scores, e.g.
 Tf.idf (cf. Manning/Schütze 1999: 543)
 C-value (Frantzi et al. 2000), and many others …


 Term extraction
 Statistical scores
 Zhang et al. (2008) distinguish
 unithood measures (mutual information, log-likelihood, t-test
etc.)
 termhood measures (tf.idf, weirdness, domain pertinence,
domain specificity)
 Combined methods (e.g. C-value)
 They compare several methods


 Term extraction
 TermExtractor (Sclano and Velardi 2007) combines
several approaches
 Domain pertinence, where 𝐷 𝑖 is the domain of interest and
𝐷𝑗 is a document in another domain
 Domain consensus, where norm_freq is a normalised
frequency in a domain-specific document


 Term extraction
 TermExtractor (Sclano and Velardi 2007) combines
several approaches
 Lexical cohesion, where n is the number of words
composing a candidate and 𝑤 𝑗 a word in the candidate

 The final score is a linear combination of the three scores
 Information about structural mark-up + a set of heuristics


 Term extraction
 Nazar and Cabré (2012) present a supervised learning
approach to term extraction
 Input
 A POS-tagged list of domain terms
 A reference corpus of general language


 Term extraction
 Nazar and Cabré (2012) present a supervised learning
approach to term extraction
 Algorithm
 Calculate frequency distribution of POS sequences
 Calculate frequency distribution of lexical units (word forms and
lemmas)
 Calculate character ngrams for each word type
 Accept, in the test data, only candidates with frequent POS
patterns
 Rank candidates with frequent features higher than others


 Term alignment
 Extract term candidates from comparable multilingual
corpora and map SL terms onto TL terms
 Weller et al. (2011) deal only with neoclassical terms
(internationalisms)
 Detect candidate equivalents using string similarity
 Decompose SL candidates into morphemes (rule-based) and
translate morphemes into TL
 For compounds, split the compound first
 Check against TL candidate list


 Term alignment
 Pinnis (2013) presents a context-independent (knowledgepoor) method for term mapping
 Pre-processing
 Lowercase candidate terms
 Apply simple transliteration rules for converting from other scripts
to Latin
 Find top N translation equivalents from a probabilistic dictionary
 Find top M transliteration equivalents using Moses character-based
MT


 Term alignment
 Pinnis (2013) presents a context-independent (resourceand knowledge-poor) method for term mapping
 Example of pre-processed terms


 Term alignment
 Pinnis (2013) presents a context-independent (resourceand knowledge-poor) method for term mapping
 Mapping
 For each token in each pre-processed term, find the longest
common substring in all other terms‘ constituents
 Otherwise, fallback on a Levenshtein-based similarity metric
 Maximise overlaps and score them

Conclusion of the session
 To sum up: You have learned about

 The role of terminology in translation and LSP
 The theoretical foundations of the discipline
 The structure, parts and basic principles of terminological
entries
 Other kinds of onomasiological resources
 Some journals, conferences and other resources
 The importance of terminological variation and methods for
finding term variants
 Semantic differences between concepts/terms that cannot be
tackled yet automatically

Conclusion of the session
 To sum up: You have learned about (continued)
 Terminological definitions, contexts and knowledge-rich
contexts
 The need for rich terminological representations and
approaches for providing them
 Some practical aspects of terminological workflows
 Knowledge-rich and knowledge-poor approaches to
term extraction and term mapping

References: Literature
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Bessé, Bruno de (1997): “Terminological definitions“. Wright, Sue Ellen / Budin, Gerhard
(eds.): Handbook of Terminology Management. Vol. 1: Basic Aspects of Terminology
Management. Amsterdam/Philadelphia: John Benjamins, pp. 63-74.
Bußmann, Hadumod (1990): Lexikon der Sprachwissenschaft. Stuttgart: Kröner.
Cabré, M. Teresa (1998): “Do we need an autonomous theory of terms?“. Terminology 5
(1), pp. 5-19.
Cramer, Irene (2011): Definitionen in Wörterbuch und Text: Zur manuellen Annotation,
korpusgestützten Analyse und automatischen Extraktion definitorischer Textsegmente im
Kontext der computergestützten Lexikographie. PhD dissertation, University of
Dortmund, Germany.
Collet, Tanja (2004): “ What’s a term? An attempt to define the term within the
theoretical framework of text linguistics”. Linguistica Antverpiensia 3, pp. 99-111.
Daille, Béatrice (2005): “Variations and application-orinted terminology engineering“.
Terminology 11 (1), pp. 181-197.
Daille, Béatrice / Habert, Benoît / Jacquemin, Christian / Royauté, Jean (1996): “Empirical
observation of term variations and principles for their description“. Terminology 3 (2),
pp. 197-257.

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Del Gaudio, Rosa / Branco, Antonio (2007): “Automatic Extraction of Definitions in
Portuguese: A Rule-Based Approach“. Neves, José / Santos, Manuel Filipe / Machado,
José Manuel (eds): Progress in Artificial Intelligence. Berlin/Heidelberg: Springer, pp. 659670.
Fahmi, Ismail / Bouma, Gosse (2006): “Learning to Identify Definitions using Syntactic
Features“. Workshop on Learning Structured Information in Natural Language
Applications at EACL 2006, Trento, Italy, April 3, pp. 64-71.
Fišer, Darja / Pollak, Senja / Vintar, Špela (2010): “Learning to Mine Definitions from
Slovene Structured and Unstructured Knowledge-Rich Resources“. LREC 2010, Valletta,
Malta, May 19-21, pp. 2932-2936.
Frantzi, Katerina / Ananiadou, Sophia / Mima, Hideki (2000): “Automatic Recognition of
Multi-Word Terms: the C-value/NC-value Method“. International Journal on Digital
Libraries 3 (2), pp. 115-130.
Freixa, Judit (2006): “ Causes of denominative variation in terminology. A typology
proposal”. Terminology 12 (1), pp. 51-77.
Geeraerts, Dirk (2010): Theories of Lexical Semantics. Oxford: Oxford University Press.

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Manning, Christopher D. / Schütze, Hinrich (1999): Foundations of statistical natural
language processing. Cambridge: MIT Press.
Meyer, Ingrid (2001): “ Extracting Knowledge-Rich Contexts for Terminography: A
conceptual and methodological framework”. Bourigault, Didier / Jacquemin, Christian /
L’Homme, Marie-Claude (eds.): Recent Advances in Computational Terminology.
Amsterdam/Philadelphia: John Benjamins, pp. 279-302.
Malaisé, Véronique / Zweigenbaum, Pierre / Bachimont, Bruno (2005): “Mining defining
contexts to help structuring differential ontologies”. Terminology 11 (1), pp. 21-53.
Marshman, Elizabeth (2008): “ Expressions of uncertainty in candidate knowledge-rich
contexts”. Terminology 14 (1), pp. 124-151.
Muresan, Smaranda / Klavans, Judith (2002): “A Method for Automatically Building and
Evaluating Dictionary Resources”. LREC 2002, Las Palmas, Spain, May 29-31, pp. 231-234.
Nazar, Rogelio / Cabré, Maria Teresa (2012): “Supervised Learning Algorithms Applied to
Terminology Extraction“. TKE 2012, Madrid, Spain, June 19-22, pp. 209-217.
Pearson, Jennifer (1998): Terms in Context. Amsterdam/Philadelphia: John Benjamins.
Pinnis, Mārcis (2013): “Context Independent Term Mapper for European Languages“.
RANLP 2013, Hissar, Bulgaria, September 7-13, pp. 562-570.

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Przepiórkowski, Adam / Degórski, Łukasz / Spousta, Miroslav / Simov, Kiril / Osenova,
Petya / Lemnitzer, Lothar / Kuboň, Vladislav / Wójtowicz, Beata (2007): “Towards the
Automatic Extraction of Definitions in Slavic“. BSNLP workshop at ACL 2007, Prague,
Czech Republic, June 29, pp. 43-50.
Sclano, Francesco / Velardi, Paola (2007): “TermExtractor: a Web Application to Learn
the Shared Terminology of Emergent Web Communities“. TIA 2007, Sophia Antipolis,
France, October 8-9.
Schmitt, Peter A. (1999): Translation und Technik. Tübingen: Stauffenburg.
Schumann, Anne-Kathrin (2013): “Collection, Annotation and Analysis of Gold Standard
Corpora for Knowledge-Rich Context Extraction in Russian and German“. Student
workshop at RANLP 2013, Hissar, Bulgaria, September 7-13, pp. 134-141.
Sierra, Gerardo / Alarcón, Rodrigo / Aguilar, César / Bach, Carme (2008): “Definitional
verbal patterns for semantic relation extraction”. Terminology 14 (1), pp. 74-98.
Storrer, Angelika / Wellinghoff, Sandra (2006): “Automated detection and annotation of
term definitions in German text corpora”. LREC 2006, Genoa, Italy, May 24-26, pp. 23732376.

 Weller, Marion / Gojun, Anita / Heid, Ulrich / Daille, Béatrice / Harastani,
Rima (2011): “Simple methods for dealing with term variation and term
alignment“. TIA 2011, Paris, France, November 8-10, pp. 87-93.
 Westerhout, Eline (2009): “Definition Extraction using Linguistic and
Structural Features“. First Workshop on Definition Extraction at RANLP
2009, Borovets, Bulgaria, September 14-16, pp. 61-67.
 Wüster, Eugen (1985): Einführung in die Allgemeine Terminologielehre und
terminologische Lexikographie. 2nd edition. Wien: Infoterm.
 Zhang, Ziqi / Iria, José / Brewster / Christopher, Ciravegna, Fabio (2008):
“A Comparative Evaluation of Term Recognition Algorithms“. LREC 2008,
Marrakech, Morocco, May 28-30, pp. 2108-2113.

References: Tools and Resources
 www.isocat.org
 iate.europa.eu

Contributions to this Presentation

 Prof. Klaus-Dirk Schmitz, Cologne University of Applied Sciences
 Thanks to Dr. Alessandro Cattelan for backing me up!

The end End.
Thanks for your attention!

17. Anne Schuman (USAAR) Terminology and Ontologies 2

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