Terminology work and term databases in Estonia
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The document discusses different approaches to structuring terminology data, including semasiological (word-meaning) and onomasiological (concept-label) structures. It notes limitations with binary, introspective models and outlines quantitative, empirically-based structures using metrics like relatedness graphs. The presentation concludes that smart dictionaries provide hints while dumb users learn, versus definitive answers from smart dictionaries for dumb users.
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Terminology work and term databases in Estonia
- 1. Terminology work and term databases in Estonia With emphasis on termbase data structures Arvi Tavast, PhD qlaara Riga, 4 November 2015
- 2. Lexicography Terminology Whats wrong Quantitative Introduction From Estonian terminology to termbase data structures We used to have specialised lexicography that people a鍖ectionately called terminology Then we had a bit of terminology (even applied to general language) There were calls for a uni鍖ed termbase of all terms Which is unfortunately not doable: coverage reliability lack of convention theoretical issues The following presentation gives a bit more detail
- 3. Lexicography Terminology Whats wrong Quantitative Outline 1 Lexicography: semasiological data structures 2 Terminology: onomasiological data structures 3 Whats wrong Data structures Metaphors of communication 4 Quantitative dictionary data structures Data structures Division of labour
- 4. Lexicography Terminology Whats wrong Quantitative Semasiological data structures Words and what they mean en: table 1. a piece of furniture with four legs and a 鍖at top de: Tisch 2. layout of data in rows and columns de: Tabelle en: desk - an o鍖ce table de: Tisch de: Schreibtisch en: spreadsheet - a data layout consisting of rows and columns de: Tabelle de: Arbeitsblatt
- 5. Lexicography Terminology Whats wrong Quantitative Onomasiological data structures Concepts and how they are called 1 A piece of furniture with four legs and a 鍖at top, for eating en: table de: Tisch 2 A piece of furniture with four legs and a 鍖at top, for writing en: desk de: Tisch de: Schreibtisch 3 Layout of data in rows and columns en: table en: spreadsheet de: Tabelle de: Arbeitsblatt
- 6. Lexicography Terminology Whats wrong Quantitative Example Latvian-Estonian dictionary
- 7. Lexicography Terminology Whats wrong Quantitative Example Latvian-Estonian dictionary
- 8. Lexicography Terminology Whats wrong Quantitative Whats wrong Data structures Semasiology Pro: easy for the editor, understandable for the reader Con: no support for consistency A narrative about the editor, not a data source about language Onomasiology Pro: consistency, scalability, standardisation Con: need for explicit binary decisions An oversimpli鍖ed data source about language; works if concepts are known Both Binary: either means or does not mean, there is no scale Introspective: claims are not falsi鍖able Simplistic: assume the concepts are (or can be) known The channel metaphor of communication
- 9. Lexicography Terminology Whats wrong Quantitative Whats wrong The channel metaphor vs uncertainty reduction Encoding of a message must contain a set of discriminable states that is greater than or equal to the number of discriminable states in the to-be-encoded message or: Encoding thoughts with words can only work if the number of possible thoughts is smaller than or equal to the number of possible words This is the case only in very restricted domains (e.g. weather forecasts) Ramscar, M. et al. 2010. The E鍖ects of Feature-Label-Order and Their Implications for Symbolic Learning. Cognitive Science 34(6): 909957.
- 10. Lexicography Terminology Whats wrong Quantitative Quantitative data structures Words (lexomes), their relatedness and other numerical parameters Empirical data sources, rather than introspective Corpus research, frequencies, collocations, distributional semantics Human experimental judgements NB Meaning is inherently introspective, not measurable. Relative meaning is measurable Quanti鍖ed data, rather than binary Types of relatedness: synonyms, equivalents, cohyponyms, etc. Other numerical parameters: frequency, valence, emotion, reaction times, naming latencies, neighbourhood density, relative entropy, median absolute deviation, morphological distribution, search statistics etc.
- 11. Lexicography Terminology Whats wrong Quantitative Quantitative data structures Relatedness can be quanti鍖ed and presented as a graph or a table table1 table2 desk spreadsheet Tisch Schreibtisch Tabelle Arbeitsblatt table1 1 0 0.1 0 0.6 0.4 0 0 table2 0 1 0 0.5 0 0 0.8 0.8 desk 0.1 0 1 0 0.6 0.8 0 0 spreadsheet 0 0.5 0 1 0 0 0.7 0.8 Tisch 0.6 0 0.6 0 1 0.8 0 0 Schreibtisch 0.4 0 0.8 0 0.8 1 0 0 Tabelle 0 0.8 0 0.7 0 0 1 0.8 Arbeitsblatt 0 0.8 0 0.8 0 0 0.8 1 Fictional data for demonstration purposes only
- 12. Lexicography Terminology Whats wrong Quantitative Division of labour Dumb user, smart dictionary vs smart user, dumb dictionary A smart dictionary provides the correct answers A dumb dictionary provides hints, like a thesaurus or synonym dictionary A dumb user looks for de鍖nite answers A smart user can 鍖gure out the answer based on even subtle hints
- 13. Lexicography Terminology Whats wrong Quantitative Thanks for listening Contacts and recommended reading 際際滷s: www.slideshare.net/arvitavast Contact: arvi@qlaara.com Easy reading: blog.qlaara.com Pointer to the real stu鍖: Ramscar, M. et al. 2010. The E鍖ects of Feature-Label-Order and Their Implications for Symbolic Learning. Cognitive Science 34(6): 909957