17. Anne Schuman (USAAR) Terminology and Ontologies 2


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17. Anne Schuman (USAAR) Terminology and Ontologies 2

  1. 1. Terminology and Ontologies Section 2: Current Research Topics Anne-Kathrin Schumann Saarland University “Expert“ Winter School Birmingham November 13, 2013
  2. 2. Overview  Current trends in research  Term variation  Culture-specific semantic differences  Definitions, contexts, knowledge-rich contexts  Usability aspects  Term extraction and term mapping
  3. 3. 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)
  4. 4. Current trends in research  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)
  5. 5. Current trends in research  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)
  6. 6. Current trends in research  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
  7. 7. Current trends in research  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
  8. 8. Current trends in research 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
  9. 9. Current trends in research  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
  10. 10. Current trends in research  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)
  11. 11. Current trends in research  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
  12. 12. Current trends in research  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
  13. 13. Current trends in research  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)
  14. 14. Current trends in research  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)
  15. 15. Current trends in research  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
  16. 16. Current trends in research  Variant recognition given a set of candidate terms:  term variation patterns for rule-based variant recognition (Weller et al. 2011)
  17. 17. Current trends in research  Culture-specific semantic differences  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
  18. 18. Current trends in research  Culture-specific semantic differences  Schmitt (1999) mentions different types of semantic differences on the CONCEPTUAL level, e.g.  culture-dependent differences between conceptual hierarchies  culture-dependent semantic prototypes
  19. 19. Current trends in research  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?
  20. 20. Current trends in research carbon steel = Baustahl (+ term variation …) “Most dictionaries fail to provide accurate descriptions, especially in problematic cases …“ (Schmitt 1999: 219, my translation from German)
  21. 21. Current trends in research  Culture-specific semantic differences  culture-dependent semantic prototypes • 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.
  22. 22. Current trends in research  Culture-specific semantic differences  culture-dependent semantic prototypes “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)
  23. 23. Current trends in research  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).
  24. 24. Current trends in research  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?
  25. 25. Current trends in research  Definitions, contexts, knowledge-rich contexts (ISOCat)
  26. 26. Current trends in research  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.
  27. 27. Current trends in research  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
  28. 28. Current trends in research  Definitions, contexts, knowledge-rich contexts  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)
  29. 29. Current trends in research  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
  30. 30. Current trends in research  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)
  31. 31. Current trends in research  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
  32. 32. Current trends in research  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
  33. 33. Current trends in research
  34. 34. Current trends in research
  35. 35. Current trends in research
  36. 36. Current trends in research
  37. 37. Current trends in research
  38. 38. Current trends in research  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).
  39. 39. 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)
  40. 40. Term extraction and term mapping  Term extraction  Statistical scores, e.g.  Tf.idf (cf. Manning/Schütze 1999: 543)  C-value (Frantzi et al. 2000), and many others …
  41. 41. Term extraction and term mapping  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
  42. 42. Term extraction and term mapping  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
  43. 43. Term extraction and term mapping  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
  44. 44. Term extraction and term mapping  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
  45. 45. Term extraction and term mapping  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
  46. 46. Term extraction and term mapping  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
  47. 47. Term extraction and term mapping  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
  48. 48. Term extraction and term mapping  Term alignment  Pinnis (2013) presents a context-independent (resourceand knowledge-poor) method for term mapping  Example of pre-processed terms
  49. 49. Term extraction and term mapping  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
  50. 50. 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
  51. 51. 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
  52. 52. 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.
  53. 53. References: Literature       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.
  54. 54. References: Literature         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.
  55. 55. References: Literature       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.
  56. 56. References: Literature  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.
  57. 57. References: Tools and Resources  www.isocat.org  iate.europa.eu
  58. 58. Contributions to this Presentation  Prof. Klaus-Dirk Schmitz, Cologne University of Applied Sciences  Thanks to Dr. Alessandro Cattelan for backing me up!
  59. 59. The end End. Thanks for your attention!