Prof Klaus: Terminology Management

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Prof Klaus: Terminology Management

  1. 1. Terminology Managementin Technical Communication tcworld India Bangalore, 11 – 12 March 2011 Klaus-Dirk Schmitz Institute for Information Management Faculty 03 University of Applied Sciences Cologne klaus.schmitz@fh-koeln.de
  2. 2. Overview What is terminology? Basic principles of terminology management Terminology management in companies Tools for managing terminology  Terminology extraction tools  Terminology databases  Terminology control tools K.-D. Schmitz, IIM, FH Köln
  3. 3. What is terminology ?Note: If you omit the password, MultiTermprompts you for a password when loading,assuming the database is password-protected.If you log on as the system administrator, youare normally asked whether you want exclusiveaccess to the database. This is not the case whenopening a database using parameters; in thiscase, it is assumed that you do want exclusiveaccess. Only when exclusive access is notavailable, MultiTerm does assume that you stillwant to take part in normal multi-user operation. K.-D. Schmitz, IIM, FH Köln
  4. 4. What is terminology ?Note: If you omit the password, MultiTermprompts you for a password when loading,assuming the database is password-protected.If you log on as the system administrator, youare normally asked whether you want exclusiveaccess to the database. This is not the case whenopening a database using parameters; in thiscase, it is assumed that you do want exclusiveaccess. Only when exclusive access is notavailable, MultiTerm does assume that you stillwant to take part in normal multi-user operation. K.-D. Schmitz, IIM, FH Köln
  5. 5. What is terminology ?Note: If you omit the password, MultiTermprompts you for a password when loading,assuming the database is password-protected.If you log on as the system administrator, youare normally asked whether you want exclusiveaccess to the database. This is not the case whenopening a database using parameters; in thiscase, it is assumed that you do want exclusiveaccess. Only when exclusive access is notavailable, MultiTerm does assume that you stillwant to take part in normal multi-user operation. K.-D. Schmitz, IIM, FH Köln
  6. 6. What is terminology ?database terminologyexclusive access =loading vocabulary oflog on a subject fieldMultiTerm * =multi-user operation Gesamtheit der Begriffeopen a database und Benennungen inparameter einem Fachgebiet (DIN 2342)password =password-protected set of designations belongingprompt to one special languagesystem administrator (ISO 1087-1) K.-D. Schmitz, IIM, FH Köln
  7. 7. Terminological Triangle concept„mouse“ term object K.-D. Schmitz, IIM, FH Köln
  8. 8. Object Any part of the perceivable or conceivable world Objects may be material (e.g. mouse) or immaterial (e.g. magnetism) K.-D. Schmitz, IIM, FH Köln
  9. 9. Concept Unit of thinking made up of characteristics that are derived by categorizing objects having a number of identical properties (DIN) Unit of knowledge created by a unique combination of characteristics (ISO) Concepts are not bound to particular languages. They are, however, influenced by social or cultural background K.-D. Schmitz, IIM, FH Köln
  10. 10. “mouse”Term Designation of a defined concept in a special language by a linguistic expression Designation: Any representation of a concept A term may consist of one or more words A term may consist of one or more words Single word term: mouse, printer, laser Multi-word term: laser printer, printer with single-sheet feed K.-D. Schmitz, IIM, FH Köln
  11. 11. Synonymy  Communication can be disturbed! “return key?” “enter key” K.-D. Schmitz, IIM, FH Köln
  12. 12. Synonymy Synonymy exists if two or more terms in a given language represent the same concept. K.-D. Schmitz, IIM, FH Köln
  13. 13. Synonymy K.-D. Schmitz, IIM, FH Köln
  14. 14. Homonymy / Polysemy  Communication can fail! “mouse” K.-D. Schmitz, IIM, FH Köln
  15. 15. Homonymy / Polysemy Polysemy: etymological affinity, the same word. Homonymy exists if one term or several terms that have the same external form refer to several concepts. True homonymy: different words with the same form, no etymological affinity. Differentiation between polysemy and homonymy is irrelevant for terminology work. K.-D. Schmitz, IIM, FH Köln
  16. 16. Coining new terms K.-D. Schmitz, IIM, FH Köln
  17. 17. Term-related issues Coining of terms: word formation + term buildings mechanisms  Composition: cyberspace, translation memory system  Derivation: preface, management  Conversation: the chair  to chair, green (adj)  the green  Terminologization: mouse (IT)  mouse (bio, general), virus (IT)  virus (med)  Loan word: festschrift, zeitgeist from DE, rickshaw from JP  Abbreviation: CEO, AIDS, scuba, Interpol  New creation: blurb, quark (very rare) K.-D. Schmitz, IIM, FH Köln
  18. 18. Selecting “good” terms• USB flash drive• flash drive• USB stick• USB memory key• memory stick• keydrive• pendrive• thumbdrive• jumpdrive• etc. K.-D. Schmitz, IIM, FH Köln © Prof. Dr. Petra Drewer & Prof. Dr. Klaus‐Dirk Schmitz
  19. 19. Term-related issues Criteria for the selection and creation/coining of terms:  Transparency/motivation  Consistency  Appropriateness  Linguistic economy  Derivability  Linguistic correctness  Preference for native language K.-D. Schmitz, IIM, FH Köln
  20. 20. Transparency The concept designated by the term can be inferred without a definition (e.g. pipe wrench or adjustable wrench vs. monkey wrench) The meaning of the term is visible by:  morphological motivation page setup, error message data network identification code* network printing device setup*  semantic motivation worm, virus, infected file, vulnerability, firewall* K.-D. Schmitz, IIM, FH Köln
  21. 21. Consistency Terminology must be defined accurately and used consistently at least within:  one document  one product  one company or organization Only one term for each concept (avoid synonyms !) Only one concept for each term (avoid homonyms !) But also consistent term coining (see Wikipedia: wrench) K.-D. Schmitz, IIM, FH Köln
  22. 22. Appropriateness Appropriateness means that terms:  have to be familiar to the user (localization!)  don’t cause confusion or insecurity  have no negative connotations (neutral, politically correct)  express installation (only components needed) network installation (all components)  system error, severe error, fatal error, user error etc.  master/slave, web designers’ bible, knowledge nugget etc.  nuclear energy vs. atomic energy K.-D. Schmitz, IIM, FH Köln
  23. 23. Appropriateness K.-D. Schmitz, IIM, FH Köln
  24. 24. Other features Linguistic economy  Ultrakurzwellenüberreichweitenfernsehrichtfunkverbindung Derivability  medicinal plant vs. herb  herbal, herbalist, ...  Bedeutungslehre  Semantik Linguistic correctness  aktualisieren vs. updaten, geupdated, upgedatet, ...  OpenSource, Cafe ToGo, fünfköpfiger Familienvater, … Preference for native language  Startseite vs. Homepage, Multifunktionsleiste vs. Ribbon K.-D. Schmitz, IIM, FH Köln
  25. 25. Overview What is terminology? Basic principles of terminology management Terminology management in companies Tools for managing terminology  Terminology extraction tools  Terminology databases  Terminology control tools K.-D. Schmitz, IIM, FH Köln
  26. 26. Terminology in companies Terminology is an important carrier of knowledge for domain-specific information in companies Within a company, but also between company and customers as well as between company and suppliers Many departments and sectors of a company create, disseminate, retrieve and use terminology BUT: Terminology management is discussed controversially:  Costs + effort  Quality and efficiency Therefore: Costs and benefits of terminology management K.-D. Schmitz, IIM, FH Köln
  27. 27. tekom survey Successful terminology management in companies Practical tips and guidelines: Basic principles, implementation, cost-benefit analysis, system overview published in German 4/2010, about 300 pages, CD with data also available in English K.-D. Schmitz, IIM, FH Köln
  28. 28. tekom survey Online questionnaire end of 2009 about 1,000 sent out, mostly to tekom members High response rate of 940 questionnaires (77% tekom) 34% managerial staff and CEOs 64% employees 67% industrial enterprises 15% software companies 13% service providers (TD / translation / localization) (And: questionnaire for tools providers, questionnaire for benchmarking companies (25), 2 benchmarking workshops) K.-D. Schmitz, IIM, FH Köln
  29. 29. tekom survey: the situationAt an average, a company has to manage 11.81 different information products is creating 5.87 different technical documentations has to deal with the fact, that 5.04 different sections/ departments are involved in the process of creating (new) terms is translating information products into 10.1 different languages K.-D. Schmitz, IIM, FH Köln
  30. 30. Product planning and R&D & Product Management  Product specifications development R&D  Procedural instructions  Process documentation for product development  CAD graphics, 3D-models pertaining to the product  Laboratory manuals R&D & software development S oftware GUIs / user menus R&D & TD marketing  Images pertaining to the product R&D & TD  Data sheets  Parts lists R&D & Marketing  Packaging labels / product labels QM  Quality documentation Product marketing R&D & Product management  Information pertaining to product release changes Marketing and product management  Product information sheets for pre-sales Marketing  Marketing material  Customer presentations Sales and Marketing &TD  Product catalogues  Price catalogues  Contract documents Corporate communications  Press releases Product usage Technical Communication (TD)  Montage and installation instructions  Commissioning manuals  Online help Training & TD  Training documents Marketing and TD  Multimedia/simulation programs TD & R&D & software  dev. User interfaces(GUIs) / software descriptions R&D  Control elementsand labelsProduct maintenance TD  Maintenance/S ervice manuals TD & Service R epair manuals S pare parts catalogues K.-D. Schmitz, IIM, FH Köln
  31. 31. Who is creating terminology? Multiple answers, Technical documentation 79.7% average 5.04 Research / (software) development / 79.7% engineering different Marketing 63.5% departments/ Product management/ Portfolio 61.3% sections management Translation / Localization 40.4% Distribution / Sales 39.3% Customer service / After sales 30.7% Training 28.4% Management board 26.3% Corporate communications / Public 24.4% relation Quality assurance / Quality management 16.3% Purchase / Procurement 12.3% Montage / Assembly planning / 12.3% Production Servicing / Maintenance 8.2% IT service 6.7% K.-D. Schmitz, IIM, FH Köln
  32. 32. Terminology problems 84.2 % report, that always or very frequently various departments/sections use different terms for the same concept 70.7 % report, that always or very frequently differing terms for the same concept are used in various documents 47.1 % of the staff always or very frequently have problems in understanding technical terms on the spot 51.1 % of the staff always or very frequently have to ask for or retrieve the correct term for a given concept K.-D. Schmitz, IIM, FH Köln
  33. 33. Consequences: Terminology problems K.-D. Schmitz, IIM, FH Köln
  34. 34. Opinions about terminology 96,9% 96,0% 96,0% 87,7% sehen die Zeitersparnis in halten die Making work schätzen die Improving Better gehen von einer eher großen Saving time Arbeitserleichterung für eher Qualitätsverbesserung von bis sehr großen Erleichterung der Kommunikation und understandingArbeit als eher groß bis sehr easier groß bis sehr groß quality Dokumenten und in der der Verständlichkeit für den for the customer groß an Kommunikation als eher Kunden aus groß bis sehr groß K.-D. Schmitz, IIM, FH Köln
  35. 35. Terminology documentationDocumentation of terminologyDefinitions 84.3%Subject field information 78.2%Status (preferred, admitted, deprecated, do not use etc.) 72.3%Grammatical information (Gender, POS, Number etc.) 51.4%Project, product, customer, department information 44.9%Illustrations 34.8% And: Context, Example, Synonym, No-Term, Source, Explanation, References, Position numbers, short forms K.-D. Schmitz, IIM, FH Köln
  36. 36. Model for a cost-benefit analysis1. Analysis of the problem2. Analysis of the impact (very often, very expensive, bad quality)3. Framework conditions for the value of benefit (many documents, high volumes, many languages, using TMS, using CMS)4. Definition of goals (management triangle: costs, time, quality)5. Analysis of benefit (consistent terminology, less changes, higher TM match rate, less queries by translators)6. With/without comparison7. Analysis of costs (initial costs: system, implementation, training; running costs: licenses, personnel, working hours)8. Cost-benefit analysis9. Success factors and risks (early, involve all, workflow) K.-D. Schmitz, IIM, FH Köln
  37. 37. Key performance indicators: benefitsFor a specific application scenario: Costs for changes of terms in the source language Costs for queries from translators Costs for terminology related translation corrections Translation costse.g. for changes of terms: duration in number of hours of work for making changes wages for the hours of work number of changes made per document number of newly created documents per yearAnd: when changes? in which formats/systems? with/without termbase! K.-D. Schmitz, IIM, FH Köln
  38. 38. Key performance indicators: costsFor a specific application scenario: Procurement costs for a termbase system (12,000-40,000 €) Costs for system support and update (1,100-9,000 €) Time for one SL term entry (ø 30 min) plus TL info (ø 20 min) Number of new entries/year (60-600) + updated entries (100-1200) Monthly salaries for the terminology staff K.-D. Schmitz, IIM, FH Köln
  39. 39. Cost-benefit analysis Type of Problems Degree of Necessity Degree of Effects e.g. Number of Languages Framework Conditions e.g. Amount of Translation for Optimum Usage e.g. TMS Usage e.g. Number Employees TD Benefit Key Indicators: Cost Key Indicators: e.g. Costs for Source Text Changes e.g. Investment Costs for TermBase SystemCosts for Answering Translators Questions Costs for Training Personnel Costs for Target Text Corrections Running Costs for Terminology Management TMS Match Rates System Maintenance Alternatives Without Defined With Defined (Without Defined With Defined Terminology Terminology Terminology) Terminology Figures to Compare from Benchmarking or Estimation Evaluation and Comparison Benefit Evaluation and Comparison Costs K.-D. Schmitz, IIM, FH Köln
  40. 40. Cost-benefit analysis: sample + 200 000 € 1. Year 2. Year 3. Year 4. Year + 150 000 € Corrections Corrections Corrections Break- Even- Translation Translation Point Translation + 100 000 € costs costs costs + 50 000 € Translator Translator Translator queries queries queries Changes Changes Changes - 50 000 € Salary Salary Salary Salary expenses expenses expenses expenses - 100 000 € Licenses ROI Licenses Licenses Licenses Initial investment - 150 000 € - 200 000 € K.-D. Schmitz, IIM, FH Köln
  41. 41. Pain curve for terminology management Source: Dunne, Keiran; Multilingual, April 2006 K.-D. Schmitz, IIM, FH Köln
  42. 42. Terminology error propagation Source: Dunne, Keiran;  Multilingual, April 2006 K.-D. Schmitz, IIM, FH Köln
  43. 43. Source: Childress, Mark;  Multilingual, April 2006Terminology error propagation K.-D. Schmitz, IIM, FH Köln
  44. 44. Overview What is terminology? Basic principles of terminology management Terminology management in companies Tools for managing terminology  Terminology extraction tools  Terminology databases  Terminology control tools K.-D. Schmitz, IIM, FH Köln
  45. 45. Terminology extraction In many application scenarios of terminology work, the extraction of terminology from (existing) textual material is recommended. We can differentiate between the following extraction methods:  Monolingual term extraction (text in electronic form)  Bilingual term extraction (parallel aligned texts, i.e. TMs)  Manual (human) term extraction  Computer-assisted term extraction (tools propose term candidates) • With statistical methods (for “all” languages, cannot use knowledge about syntax) • With linguistic methods (better results, but only for “important” languages) • With hybrid methods (combining statistical and linguistic methods) K.-D. Schmitz, IIM, FH Köln
  46. 46. Terminology extraction tools Features of (monolingual) term extraction tools:  Common functionalities from concordance programs (e.g. WordSmith): identify words, word statistics, KWIC index, alphabetic/frequency order  Reducing inflected word forms to the basic canonical form: needed for real statistics, needs morphological knowledge  Filtering and ignoring function words (articles, conjunctions etc.) and general language words (but what is general language?)  Filtering and ignoring terms that are already included in a term base  Identifying multi-word terms, noun phases and verbal phases  Identifying discontinuous elements and elliptical constructions K.-D. Schmitz, IIM, FH Köln
  47. 47. Terminology extraction tools Improving and enriching term candidates with SDL MultiTerm Extract K.-D. Schmitz, IIM, FH Köln
  48. 48. Terminology extraction tools Settings to improve the results of term extraction with SDL MultiTerm Extract K.-D. Schmitz, IIM, FH Köln
  49. 49. Terminology extraction tools Benefits and problems of term extraction tools:  Term extraction tools are helpful in preparing terminology for large translation projects (with several translators) and for an initial feeding of a term base (with company or subject specific terminology)  Result of a term extraction is a list of term candidates; the list must be checked; but what about the texts (with possible not extracted terms)?  Results are only terms (and context examples), but no other terminological information; it is a kind of a to-do list for the terminologist  The more linguistics the better the results; but what about “less common” and minority languages? K.-D. Schmitz, IIM, FH Köln
  50. 50. Terminology management systems Terminology management systems are software applications that are designed to manage terminological data. They support tasks related to terminology work and store the results: Terminological data can be entered, edited, deleted, retrieved and filtered. Most of the systems available on the market are based on (relational) data base systems (MS-Access, SQL, Oracle). Can be seen as a kind of CAT-Tools (CAT=computer assisted translation). Tables in word processing or spreadsheet programs are not adequate for terminology management ! K.-D. Schmitz, IIM, FH Köln
  51. 51. Terminology management systemsClassification of terminology management systems: Complexity (languages): monolingual / bilingual / multilingual Entry structure: predefined / free-definable / hybrid Autonomy: autonomous / CAT tool component / hybrid Software technology: stand-alone / client-server / browser-based Business aspects: proprietary / commercial / open sourcee.g. SDL MultiTerm 2009 K.-D. Schmitz, IIM, FH Köln
  52. 52. Designing a terminology management solution Before designing a terminology management solution and choosing, adapting or programming a terminology management application:  Analyze the needs and objectives  Specify the user groups, tasks and workflow  Define the terminological data categories needed  Take into account the basic modelling principles  Model the terminological entry  Select, adapt, develop the software Meta data K.-D. Schmitz, IIM, FH Köln
  53. 53. Typology of data categories I Complex data categories  Open data categories content not predictable and defined by specification e.g.: term, definition, note  Closed data categories content defined by a limited set of possible values e.g.: gender, part of speech, geographical usage Simple data categories content only yes or no; values of closed data categories e.g.: masculine, noun, DE K.-D. Schmitz, IIM, FH Köln
  54. 54. Typology of data categories II Concept-oriented data categories e.g.: subject field, figure Language-oriented data categories e.g.: definition ? Term-oriented data categories e.g.: part of speech, context Administrative data categories e.g.: author, date, note Special data categories e.g.: term, language, (structural elements), (shared resources) K.-D. Schmitz, IIM, FH Köln
  55. 55. K.-D. Schmitz, IIM, FH Köln
  56. 56. Concept orientation All terminological information belonging to one concept including all terms in all languages and all term-related and administrative data must be store in one terminological entry concept = terminological entry K.-D. Schmitz, IIM, FH Köln
  57. 57. Lexicographical view / model / entry meaning meaning word meaning meanding meaning meaning K.-D. Schmitz, IIM, FH Köln
  58. 58. Terminological view / model / entry term concept term term term term term descriptive terminology management K.-D. Schmitz, IIM, FH Köln
  59. 59. Terminological view / model / entry term concept term (term) term term term prescriptive terminology management K.-D. Schmitz, IIM, FH Köln
  60. 60. Lexicographical entry K.-D. Schmitz, IIM, FH Köln
  61. 61. Terminological entry K.-D. Schmitz, IIM, FH Köln
  62. 62. Terminological entry K.-D. Schmitz, IIM, FH Köln
  63. 63. Eintragsmodellierung + Prinzipien K.-D. Schmitz, IIM, FH Köln
  64. 64. Term autonomy All terms belonging to one concept should be managed (in one terminological entry) as autonomous (repeatable) blocks of data categories without any preference for a specific term  Therefore all terms can be documented with the relevant term-related data categories  Term autonomy is necessary for the main term, all synonyms, all variants, and all short forms  Term autonomy is not explicitly discussed in theoretical literature K.-D. Schmitz, IIM, FH Köln
  65. 65. Concept orientation & term autonomyTermEntry Concept represented by ID-No. and/or classification / notation Language 1 Language 2 Language 3 ... + AuxInfo + AuxInfo + AuxInfo Term 1 Term 1 Term 1 + AuxInfo + AuxInfo + AuxInfo Term 2 Term 2 + AuxInfo + AuxInfo Term 3 + AuxInfo K.-D. Schmitz, IIM, FH Köln
  66. 66. Concept orientation & term autonomy K.-D. Schmitz, IIM, FH Köln
  67. 67. Terminological data modeling Terminological data categories  ISO 12620:1999 / 12620:2009  definition, subject field, grammar, context, project code, author, date etc.  Data Category Registry (DCR) Terminological data modeling principles  ISO 12200 / 16642 / 30042  meta model, concept orientation, term autonomy, TBX (Termbase eXchange) K.-D. Schmitz, IIM, FH Köln
  68. 68. Terminology control In many application scenarios of terminology work, the checking of correct and consistent use of terminology in documents (created by technical writers or translators) is recommended. We can differentiate between the following control methods:  Monolingual terminology control  Bilingual terminology control (for translations)  Manual (human) terminology control (part of proof reading & QA)  Computer-assisted term control (tools analyze and check documents) • Without linguistic methods (for “all” languages, using the content of a term base) • With linguistic methods (better results, but only for “important” languages) K.-D. Schmitz, IIM, FH Köln
  69. 69. Terminology control tools Features of terminology checking tools:  Similar to spell checkers and auto correction  Integrated into editors, authoring systems, CAT tools, but also as stand-alone programs  Directly during the writing process of a document or translation, or as an autonomous process (when the document is finished)  Connection to the term base entries (interactive or via export/import)  Very often combined with grammar and style checking (controlled language)  Using fuzzy search and/or linguistics (inflected terms in texts vs. canonical form of the terms in term bases)  Deprecated terms must be maintained in the term base (no-terms) K.-D. Schmitz, IIM, FH Köln
  70. 70. Terminology control toolsSample of a terminology check with acrolinx IQ Suite K.-D. Schmitz, IIM, FH Köln
  71. 71. Terminology control tools Automatic detection of linguistic variants with acrolinx IQ Suite K.-D. Schmitz, IIM, FH Köln
  72. 72. Conclusion 1 You will find terminology problems in many companies In any case, terminology management will improve important factors such as: better communication, consistent corporate language, avoidance of errors and misunderstanding, faster training, better translations Not all benefits are easy to calculate and quantify Benefits and ROI depend on several company specific framework conditions Terminology management is no luxury, it is a necessity for all industrial companies and service providers, and this can be documented by key indicators K.-D. Schmitz, IIM, FH Köln
  73. 73. Conclusion 2 Only excellent managed terminology can guarantee a high quality information and communication:  follow guidelines for term creation and term selection  model your termbase with concept orientation and term autonomy, and include appropriate data categories for documenting terms Wrong decisions and mistakes can later be repaired only with huge efforts and costs ! K.-D. Schmitz, IIM, FH Köln
  74. 74. Thank you for your attention Prof. Dr. Klaus-Dirk Schmitz Fachhochschule Köln Fakultät 03 - ITMK/IIM Mainzer Str. 5 50678 Köln klaus.schmitz@fh-koeln.de

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