1. Centre for Lexicography, Aarhus School of Business, Aarhus University, Denmark Centlex (Centre for Lexicography) TOWARDS A BETTER PERSPECTIVE IN THE SELECTION OF ENTRIES FOR AN ENGLISH DICTIONARY OF FINANCE FOR INDONESIAN STUDENTS Deny Arnos Kwary Aarhus University (Denmark) and Airlangga University (Indonesia)

2. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 2 Presentation Topics 1.Background of the Study 2.Computerized Methods in the Selection of Terms 3.Modern Theory of Lexicographical Functions 4.Conclusion

3. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 3 1. BACKGROUND OF THE STUDY  The current conditions in Indonesia:  Very few LSP dictionaries  Lack of lexicographical literature about LSP dictionaries  Needs for LSP dictionaries which can assist Indonesian learners to solve their problems in comprehending terms when they are reading their textbooks.  This presentation focuses on one of the most important aspects in making LSP dictionaries, i.e. the selection of entries.

4. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 4 Question for Lexicographers How do you select the entries for LSP Dictionaries? Case Study: English – Indonesian Dictionary of Finance

5. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 5 2. COMPUTERIZED METHODS IN THE SELECTION OF TERMS 2. 1. Vocabulary Classifications 2. 2. Key Word Analysis 2. 3. Term Extraction

6. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 6 2. 1. Vocabulary Classifications  Four kinds of vocabulary in a text: High frequency words, Academic words, Technical words, and Low-frequency words (Nation 2001: 11-13)  Word Lists used as Stop Lists in the RANGE software: Word List One The first 1000 words from the General Service List (West 1953) High Frequency Words Word List Two The second 1000 words from the General Service List (West 1953) High Frequency Words Word List Three 570 headwords from the Academic Word List (Coxhead, 2000) Academic Words

7. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 7 Results from RANGE Software (1) The sample text is taken from the CFA (Chartered Financial Analyst) textbook, Study Session 8, Book 3, Level 1. WORD LISTTOKENS/%TYPES/%FAMILIES one15990/67.41837/46.09 493 two1404/ 5.92170/ 9.36104 three2846/12.00378/20.81217 not in the lists3479/14.67 431 /23.73 ????? Total23719 1816 814

8. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 8 Results from RANGE Software (2) NO.BASE ONE FAMILIESTYFREQFAFREQF1 1.THE1539 2.OF862 3.AND587 12OR190 13EXPENSE128197 14COST121165 15NOT121124 16 STOCK 121123

9. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 9 Results from RANGE Software (3)  Nation (2001: 18) actually realized that some technical vocabulary actually also occurs in the high frequency words. Therefore, he suggested comparing the frequency of words in a specialized text with their frequency in a general corpus.  Is this an effective solution?

10. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 10 2. 2. Key Word Analysis  Key word: “a word which occurs with unusual frequency in a given text” (Scott 1997: 236).  Based on this definition, a key word does not always mean a word with high frequency.  If the occurrence of a word is outstandingly frequent in a target corpus than in a reference corpus, it will be considered a positive key word.  All words are ordered in terms of their relative keyness.

11. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 11 Using a Key Word Analysis  Target corpus: CFA textbook, Study Session 8, Book 3, Level 1 (23,719 words).  Reference corpus: the British Academic Written English (BAWE) corpus (6,506,995 words).  The analysis can be done by using a keyword analysis software, e.g. Wordsmith or AntConc.

12. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 12 Results from Key Word Analysis (1) NO.FREQUENCYKEYNESSWORD 15344445.302cash 23362191.156income 32681459.710flow 41741244.679net 51791233.703assets 61731036.930statement 71291019.977expense Is this the best solution to select the entries?

13. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 13 Results from Key Word Analysis (2) NO.FREQUENCYKEYNESSWORD 15344445.302cash 23362191.156income 32681459.710flow 41741244.679net Terms: Only single-word terms or also multi-word terms? Mostly single-word terms or mostly multi-word terms? Single-word terms, i.e. cash, income, flow, and net Multi-word terms, i.e. cash flow and net income.

14. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 14  It is more common for CFA candidates to look for the definitions of multi-word terms, such as cash flow and net income, rather than the single-word terms, i.e. cash, income, flow, and net.  The use of key word analysis alone is evidently inadequate to capture the multi-word terms.  How to effectively select entries so that multi-word terms are also included?

15. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 15 2. 3. Term Extraction  Terminologists have developed several Term Extraction programs to enable the extraction of terms automatically.  One of the programs which is available for free is TermoStat.  The program extracts not only single-word terms but also multi-word terms which have high keyness in the text.

16. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 16 FréquenceCandidat lemmatise Variantes orthographiques Poids 161cash flowcash flow, cash flows1704.69 132assetassets1343.77 92net income 868.16 76liabilityliabilities, liability755.77 112cash 742.87 51goodgoods346.04 74costcost, costs334.62 38cost of goodcost of goods302.22 The term extractor manages to capture both single-word terms and multi-word terms. Is this the best solution to select the entries?

17. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 17 FréquenceCandidat lemmatise Variantes orthographiques Poids 161cash flowcash flow, cash flows1704.69 132assetassets1343.77 92net income 868.16 76liabilityliabilities, liability755.77 112cash 742.87 51goodgoods346.04 74costcost, costs334.62 38cost of goodcost of goods302.22 What problem can you identify from this result? The Termostat can only identify multi-word terms which consist of nouns.

18. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 18 Problems of considering only nouns (1)  Based on the concordance, from 38 hits for cost of goods: 35 of them are cost of goods sold 3 of them are cost of goods available cost of goods does not stand by itself.  The problems of including cost of goods as an entry:  It is unlikely to be searched (redundancy).  It cannot be defined well in a financial sense.  It may exclude the relevant terms from being selected.

19. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 19 Problems of considering only nouns (2)  Consider the financial text: ‘to recognize an amount or an item in the financial statements.’  Oxford Advanced Learner’s Dictionary (7th ed.) defines recognize: 1.[vn] recognize sb/sth (by / from sth) to know who sb is or what sth is when you see or hear them, because you have seen or heard them or it before. 2.recognize sth (as sth) to admit or to be aware that sth exists or is true. 3.recognize sb/sth (as sth) to accept and approve of sb/sth officially. 4.[vn] be recognized (as sth) to be thought of as very good or important by people in general. 5.[vn] to give sb official thanks for sth that they have done or achieved.  Accountingdictionary.dk defines recognize: to include that amount or item as an asset or a liability in the balance sheet or as income or expense in the income statement.

20. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 20 One Important Question remains… What is the best way to select the entries of LSP dictionaries?

21. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 21 3. THE MODERN THEORY OF LEXICOGRAPHICAL FUNCTIONS  This theory has been well ingrained at the Centre for Lexicography, Aarhus School of Business, Aarhus University, Denmark, since the early 1990s, cf. Tarp (1992, 2002, 2008), Bergenholtz and Tarp (1995, 2002, 2003); Nielsen (1994) and Bergenholtz and Nielsen (2006).  A lexicographical function is the satisfaction of the specific types of lexicographically relevant need that may arise in a specific type of potential user in a specific type of extra- lexicographical situation (Tarp, 2008: 81).

22. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 22 USING THE MODERN THEORY OF LEXICOGRAPHICAL FUNCTIONS IN THE SELECTION OF ENTRIES (1)  Main lexicographical function: Text reception.  Potential users: Indonesian people who have completed undergraduate programs from a faculty of economics or a school of business and are preparing to take the CFA exams.  User situation: Reading American Financial Textbooks from the CFA Institute.  User need: Help to understand the texts they read.

23. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 23 USING THE MODERN THEORY OF LEXICOGRAPHICAL FUNCTIONS IN THE SELECTION OF ENTRIES (2) From the Classification of the Subject Field to the Terminological Classification: COMPREHENSIVE AND RELEVANT COVERAGE Principal financial statements Balance sheet Income statement Cash flow statement Assets Liabilities Fixed assets Current assets Inventories Debtors Other current assets Cash & cash equivalents Raw materials Work in process Finished goods Inventory prepayments

24. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 24 USING THE MODERN THEORY OF LEXICOGRAPHICAL FUNCTIONS IN THE SELECTION OF ENTRIES (3) From the Classification of the Subject Field to the Terminological Classification: SURPASSING THE COMPUTERIZED PROGRAMS Financial ratios Market ratio Activity ratio Liquidity ratio Operating ratio Profitability ratio Risk ratio Dividend cover NAV EPS P/E The computerized programs cannot identify P/E because of the slash (/)

25. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 25 4. Conclusion  Careful considerations is necessary throughout the process of selecting the entries for LSP dictionaries.  Using the computerized methods may help in lightening the burden of the lexicographers because they help to reduce the number of words to be scrutinized.  However, simply relying on those methods may lead to defective decisions in the selection of the entries for the LSP dictionary.  The implementation of the modern theory of lexicographical functions is necessary in order to augment the result of the selection process.

26. Centre for Lexicography, Aarhus School of Business, Aarhus University, DenmarkSlide 26