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

Current trends in research

Term variation

Culture-specific semantic differences

Definitions, contexts, knowledge-rich contexts

Usability aspects

Term extraction and term mapping

Overview

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


Types of variants:

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)


Types of variants: 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-specific semantic differences 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 mark-

up (“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?



(ISOCat)


Definitions, contexts, knowledge-rich contexts 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.


Definitions, contexts, knowledge-rich contexts 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)

Definitions, contexts, knowledge-rich contexts 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


Definitions, contexts, knowledge-rich contexts 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)


Definitions, contexts, knowledge-rich contexts 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

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 𝐷𝑖 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 𝑤𝑗 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 (knowledge-

poor) 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 (resource-

and knowledge-poor) method for term mapping

Example of pre-processed terms


Term alignment Pinnis (2013) presents a context-independent (resource-

and 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

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.


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. 659-670.

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.


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.


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. 2373-2376.


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

Prof. Klaus-Dirk Schmitz, Cologne University of Applied Sciences

Thanks to Dr. Alessandro Cattelan for backing me up!

Contributions to this Presentation

The end End.

Thanks for your attention!

17. anne schuman (usaar) terminology and ontologies 2

Technology

overview current trends

research terminology

term variant

term variation patterns

handling of term variation

term mapping

research cu

research types of variants