1. Tatsuhiko Matsushita LALS, Victoria University of Wellington tatsuhiko.matsushita@vuw.ac.nz

2. Main findings VDRJ is useful for designing curriculum (material, tests etc.) AWLAD The more domains a words is shared as AW or LAD by, the more abstract the meaning of the word is. general words LW Conversation and non-academic texts contain more general words and LW AWLAD Academic texts: more AW and LAD but less LW in any academic domain proper nouns low frequency words Wikipedia: more proper nouns and low frequency words AW LAD. Newspapers and academic items of Wikipedia can be a good resource for learning AW and LAD. Natural science texts contain more academic domain words at lower frequency levels than arts and social science texts LWAW LAD Origins of academic and literary words are considerably clearly separated; 3/4 of LW originate in Japanese while 3/4 of AW and LAD originate in Chinese LAD LAD contains more Western origin words (Gairaigo)

3. Contents 1. Motive for this research 2. Goals of this presentation 3. Vocabulary Database for Reading Japanese 4. Tiers of Japanese vocabulary (Basic words, academic words, limited-academic domain words, literary words) 5. Text coverage by word tier 6. Proportions of word origin types by word tiers 7. Number of characters required to cover the word tiers 7. Implications from the findings 8. Conclusion

4. 1. Motive for this research How efficiently can we learn vocabulary? Learning burden is big! More effective choice of target words More efficient order for learning the words  Effective choice and efficient order: to maximize the coverage of text which the learner would encounter in his/her domain = Reading comprehension and lexical density (Hu & Nation, 2000; Komori et al., 2004)  Q. What words should learners learn first? And second and next?

5. Studies on EAP vocabulary Basic: General Service List (West, 1953) Academic: AWL (Coxhead, 2000) UWL (Xue & Nation, 1984) EGAP-A/S, EGAP-HM/SS etc. (Tajino, Dalsky, & Sasao, 2009) Science-specific Word List (Coxhead & Hirsh, 2007) Technical: e.g. Chung (2003) Literary vocabulary?

6. Studies on JAP vocabulary Basic: The former JLPT list, Tamamura (1987) etc. Academic: Butler (2010), Matsushita (2011) ? Technical: Komiya (1995), Oka (1992) etc. Others No list for words between academic and technical words Literary vocabulary?

7. 2. Goals of this presentation To introduce I. the Vocabulary Database for Reading Japanese II. extracted domain-specific words such as Academic Words (AW), Limited-Academic-Domain Words (LAD), Literary Words (LW) To argue about IV. how the word tiers work in different types of text (register variation) V. how learner’s language background possibly affects the understanding of texts in different genres

8. 3. Vocabulary Database for Reading Japanese VDRJ Vocabulary Database for Reading Japanese （ VDRJ ） （ Matsushita, 2010; 2011 ） Created from the Balanced Contemporary Corpus of Written Japanese, 2009 monitor version (NINJAL, 2009) 33 million token (28 million from books and 5 million from the Internet forum sites (Yahoo Chiebukuro)) 19 million content words and 14 million function words Unit of counting: Lexeme – considerably inclusive but less inclusive than the word family (Level 6 in Bauer & Nation, 1993) in English “Short unit of lexemes” are ranked by U (usage coefficient) (Juilland & Chang-Rodrigues, 1964) Short unit of lexeme: more inclusive than “lemma”, less inclusive than “word family”

9. Some problems of existing Japanese word frequency lists Lack of representativeness Too old The corpus size is not large enough: low reliability for low frequency words No good sub frequency data which enable us to calculate dispersion to downgrade unevenly distributed words

10. Advantages of word lists * Various types of word lists can be created from the vocabulary database (VDRJ) A) Reference for developing vocabulary tests = Checking learners’ vocabulary levels B) Reference for checking vocabulary level of material = Checking vocabulary levels of materials C)  Specify vocabulary for learners to learn and for teachers to teach For better choice of material, modification of text Cf. Nation (2011), Word profiler

11. How to make VDRJ A) Method I. Classify all the texts into some sub corpora to see the range and dispersion cf. Nippon Decimal Classification, BCCWJ (NINJAL, 2009 ） II. Parse (made word segmentation of ) all the texts by a morphological analyzer with a dictionary (if the text is not segmented by space between words.) cf. MeCab, UniDic III. Make word lists by AntConc and/or AntWordProfiler

12. Content and construct of VDRJ Vocabulary Database for Reading Japanese The list is for reading as it is made from written corpus of books and internet forum sites Written and spoken languages are different in word frequency, domain and required language processing skills ⇒ A good corpus of spoken language is necessary to develop a good word list for it(, but there is no very good corpus of spoken Japanese…)

14. Content of the sub corpora

15. Different word rankings The word ranking problem mainly exists in Basic Words This is mainly due to lack of good spoken corpora Compromise: frequency weighted to limited domains which seem to reflect basic daily needs For International Students For General Learners Non-weighted (ranking for overall written Japanese)

16. Multidimensional scaling (MDS) 10 domains + word familiarity

17. 4. Tiers of Japanese vocabulary (1) The concept of “word tiers” Domain / Level Level = general importance = frequency × dispersion Some words are frequent only in a particular domain e.g. 発送 (shipping) 振り込み (paying by bank transfer) 古墳 (tumulus / burial mound)

18. Assumed word tiers for students Level Basic: Top 1288 = Former JLPT Level 4 &3 Intermediate: Ranked 1289-5000 Advanced 1: 6K-10K Advanced 2: 11K-15K Super-Advanced: 15K-20K 21K+ Assumed Known Words (AKW) Domain *General / Academic / Literary

19. 4. Tiers of Japanese vocabulary (2) Basic words (BW) Feature of the corpus: formal written language similar to BNC (Nation, 2004) No good spoken corpus for vocabulary studies Compromise For learners and teachers lists, the former JLPT Level 4 $ 3 vocabulary is put at the top of the list as basic words To order the basic words Identify closer domains to word familiarity (basic needs) by Multidimensional Scaling (MDS) Frequency in literary works and the Internet-forum sites (Yahoo-Chiebukuro) is weighted

20. 4. Tiers of Japanese vocabulary (3) Academic domain words Extracting academic domain words Log-likelihood ratio (LLR)(Dunning, 1993) Target texts: Technical texts Classified into four large academic domains Total number of tokens: approx. 2.9 million Reference texts: General texts in BCCWJ 2009 Total number of tokens: approx. 29.9 million Extract keywords shared by 4 - 1 domains Cut off point: higher for more narrowly distributed words

22. 4. (3) Academic domain words Academic words Academic words (AW): high specificity in 3+ academic domains 4-domain words 4-domain words (cut off point: LLR > 0) 3-domain words 3-domain words (cut off point: LLR > 0) Limited-academic-domain words Limited-academic-domain words (LAD) 2-domain words 2-domain words (cut off point: LLR > 1) 1-domain words 1-domain words (cut off point: LLR > average value) Eliminate the former JLPT Level 4 vocabulary (Top 700 words) Eliminate the words ranked at 20001 or lower Classify all the AW and LAD by word ranking levels for International Students (U=Usage Coefficient): 5 levels: Basic / Inter. / Adv. 1 / Adv. 2 / Super-adv.

24. 4. Tiers of Japanese vocabulary (3) -1 Academic words (AW) JAWL = Japanese Academic Word List High specificity in 3 or 4 academic domains 4-domain words 4-domain words (cut off point: LLR > 0) 3-domain words 3-domain words (cut off point: LLR > 0) 9 levels2590 words in total Level 0 - VIII 9 levels ， 2590 words in total JAWL I JAWL I (Intermediate): most essential for learning Basic words contains much fewer academic words JAWL I: 559 words JAWL I: 559 words Close to AWL in number and text coverage Coverage in the academic corpus used for extracting AW AWL: 10.0 ％ JAWL I: 11.1 ％

26. Distribution and examples of JAWL

27. Semantic features of AW (1) 4. (3) -1 Academic words (AW) Semantic features of AW (1) Highly abstract, essential for operating logic i.e. Range: 占める (occupy, account for), 特殊 (special, particular) Relation: 属する (belong to), 依存 (rely/reliance) Comparison/Evaluation: 後者 (the latter), 優れる (superior), Quantitative change: 減少 (decrease), 強化 (reinforce) Stage: 当初 (beginning), 現状 (present condition) Development of enunciation: 取り上げる (take up [an issue]), まとめる (summarize) Cause-effect, degree, agent, action, object, direction, goal, instrument, time etc.

28. The most frequent Kanji used for AW 合 (combine, together), 定 (fix, certain), 分 (divide, minute), 一 (one), 同 (same), 数 (number), 上 (up), 体 (body), 出 (out), 大 (large) 3-domain words: Some words have concrete meanings e.g. 署名 (signature), 保健 (health, hygiene) 4-domain words: Few words have concrete meanings The nature of the words are the same at all levels Semantic features of AW (2) 4. Tiers of Japanese vocabulary (3) -1 Academic words (AW) Semantic features of AW (2)

29. POS of Japanese AW (1) Common noun: 1072 words (41.4 %) e.g. 背景 (background) Verbal noun: 882 words (34.0 %) e.g. 連続 (establish/-ment)  Adding other types of nouns together, 81.2 %noun 2104 words ( 81.2 % ) can be a noun Verb (excluding verbal nouns): 225 words (8.7 %) e.g. 認める (recognize/approve) 述べる (describe/mention)  Adding other types of verbs together, 42.7%verb 1107 words ( 42.7% ) can be a verb Adjectival noun: 95 words (3.7 %) e.g. 詳細 (detail/-ed), 平等 (equal/-ity) Adjective9 words Adjective ： Only 9 words (0.3 %) e.g. 著しい (remarkable)

30. POS of Japanese AW (2) Affix: 106 words (4.1 %) e.g. - 期 (period), - 種 (type) substantial in Japanese academic words Adverb: 34 words (1.3 %) e.g. しばしば (frequently) Other (particle, auxiliary verb etc.): 22 words (0.8 %) Remarkably many archaic words Remarkably many archaic words e.g. のみ (only), つつ (while doing), べし (ought to), あらゆる (every) いかなる (any), 我が (my), 漠然 (vague) PassivePotentialSpontaneous れる / られる (Passive/Potential/Spontaneous) specific in academic texts specific in academic texts

31. 4. (3) -2 Limited-academic-domain words (LAD) Limited-academic-domain words Limited-academic-domain words (LAD) High specificity in 2 or 1 domain(s) 2-domain words 2-domain words (cut off point: LLR > 1) 1-domain words 1-domain words (cut off point: LLR > average value) Something between “academic” and “technical” The “scams” from extracting AW? Tiers of curriculum cf. Tajino et al. (2007) Words correspondent to the curriculum Basic: all the learners Academic words: prep. to first year Limited-academic-domain words (?): prep. to major Technical words: major to postgrad.

33. 4. (3) -2 Limited-academic-domain words (LAD) 2 domain words

34. 4. (3) -2 Limited-academic-domain words (LAD) 2 domain words

35. 4. (3) -2 Limited-academic-domain words (LAD) 2 domain words

36. Examples of 2 domain words: Words which are shared by only 2 main academic domains

37. 4. (3) -2 Limited-academic-domain words (LAD) 2 domain words Semantic features More concrete and specific than academic words Ah & Ss: Social, overlap in history and ethnology Ss & Tn: Industrial Ss & Bn: Social security, medical and nursing service Tn & Bn: Scientific Ah & Tn, Ah & Bn: not clear

38. 4. (3) -2 Limited-academic-domain words (LAD) 1 domain words It is merely a trial The corpus is not the best for academic purpose, especially for natural sciences Extracting something common across domains is much easier while extracting words by only one target corpus will require more complete target corpus Therefore, AW (4 domain words and 3 domain words) will be more reliable than LAD (2 domain words and 1 domain words)

39. 4. (3) -2 Limited-academic-domain words (LAD) 1 domain words

40. 4. (3) -2 Limited-academic-domain words (LAD) 1 domain words Semantic features are much clearer than 2 domain words

41. 4. (3) -2 Limited-academic-domain words (LAD) 1 domain words Semantic features are much clearer than 2 domain words

43. POS of Japanese LAD (1) Common noun: 1605 words (63.1 %) – more than AW (41.4%) Verbal noun : 633 words (24.9 %) e.g. 融資 (finance) cf. AW (34.0%)  Adding other types of nouns together, 87.9 %noun 2104 words ( 87.9 % ) can be a noun – more than AW (81.2%) Verb (excl. verbal nouns): 81 words (3.2 %) cf. AW (8.7%) e.g. 訳す (translate) 向き合う (face (v.))  Adding other types of verbs together, 28.1%verb 714 words ( 28.1% ) can be a verb – less than AW (42.7%) Adjectival noun: 88 words (3.5 %) cf. AW (3.7%) e.g. フル (full), 偉大 (great) Adjective3 words Adjective ： Only 3 words (0.1 %) cf. AW (0.3%) e.g. 硬い (stiff)

44. POS of Japanese LAD (2) Affix: 109 words (4.3%) cf. AW (4.1%) e.g. – 犯 (offense) substantial in Japanese academic domain words Adverb: 15 words (0.6 %) cf. AW (1.3%) e.g. 現に (surely) Other (particle, auxiliary verb etc.): 9 words (0.8 %) cf. AW (0.8%) Remarkably many archaic words Remarkably many archaic words – similar to AW e.g. なり [affirmative aux.], とも (even though), たり [affirmative aux.], ごとし (as/like), 単なる (mere), しめる（＝しむ） [causative aux.], かかる (such)

45. 4. Tiers of Japanese vocabulary (4) Literary words (LW) Extracting literary words: Extracting literary words: Words for reading literary works Log-likelihood ratio (Keyness in AntConc) Target corpus: literary works (identified by NDC and C-code) in BCCWJ 2009 (NINJAL, 2009) – Over 8 million tokens 4 different reference corpus: Technical texts, general texts in arts and humanities, general texts in the other 3 academic domains, Internet forum texts (Yahoo Chiebukuro) Extract keywords shared by the four results (Cutoff point: average value) Eliminate the former JLPT Level 4 vocabulary (Top 700 words) Eliminate the words ranked at 20001 or lower Classify all the LW by word ranking levels for International Students (U=Usage Coefficient)

46. 4. (4) Literary words (LW) Distribution and examples

47. 4. (4) Literary words (LW) POS of LW More verbs, adverbs and interjections than AW and LAD Less verbal nouns and adjectival nouns This inevitably means LW have less loan words but more Japanese-origin words.

48. 4. (4) Literary words (LW) Q. How many LW overlap with AW and LAD? Only 27 words (0.5% of academic domain words, 1.7% of LW) are overlapping Most of the overlapping words (24/27) overlap with 1 domain words (17 words overlap with words in biological natural science) Many physical words such as words for body parts e.g. 左手 (left hand), こぶし (fist), 血 (blood), 頭上 (overhead) No LW words overlap with 4 domain words Overlapping words are mainly at the intermediate level No overlapping words in or above 11K+ Some examples of overlapping words: 音 (sound), 光 (light), 棚 (shelf), 組 (class), 岩 (rock), ひざ (knee), 興奮 (excite/-ment), 全身 (whole body), 帝 (emperer), ネズミ (mouse), 帆 (sail)

49. Word tiers: In what order should students learn them? Basic General AW/LAD LW Intermediate General AW/LAD LW Advanced General AW/LAD LW Highly Advanced General AW/LAD LW Super-Advanced General AW/LAD LW Assumed known words Proper names Fillers, Signs (Transparent compounds *) Others

50. 5. Text coverage by word tier The word tier analyser: An Excel sheet where word profiling of a text can be checked automatically by cutting and pasting the result of AntWordProfiler with the word tier base word list. The word tier analyser Text covering efficiency High efficiency in vocabulary learning = Fewer unique lexemes cover more texts (Reciprocal Type/Token Ratio = Token/Type Ratio?) *Comparison should be made between equally-sized texts)

51. Coverage of Japanese texts by word tier

52. Findings from the text coverage general wordsLW Conversation and Non-academic texts: more general words and LW proper nouns low frequency words Wikipedia: more proper nouns and low frequency words AW AW Academic items of Wikipedia: 15-20% of the texts of are estimated to be covered by JAWL 1 (559 types) – encyclopaedic nature of AW?  can be a good resource for learning AW LAD AW Newspapers: similar to academic texts, but contains more LAD and AW at the advanced level AW  can be a good resource for learning AW (esp. in social sci.) AWLADLW Academic texts: more AW and LAD but less LW in any academic domain Academic texts in natural sciences: more academic domain words at lower frequency levels (technical vocabulary) than Ah. and Ss. texts – similar to Coxhead, Stevens, & Tinkle (2010)

53. 6. Proportion of word origin types by word tier Proportion of Unique Lexemes by Word Origin and Word Tier in 01K-20K (*) (Matsushita, 2011)

54. Findings from the proportion of word origin types by word tier LW LW: Japanese origin words are significantly dominant AWLAD AW and LAD: Chinese origin words are significantly dominant LAD LAD: more Western origin words (Gairaigo)  Western origin words tend to appear more at lower frequency levels in academic domain words Origins of academic and literary words are considerably clearly separated: Academic – Chinese origin Literary – Japanese origin

55. 7. Implications from the findings Q. Word Tiers: In what order should students learn them? Basic Academic LAD General Intermediate Academic LAD General Advanced Academic LAD General Highly Advanced Academic LAD General Super-Advanced Academic LAD General Assumed known words Proper names Fillers Signs (Transparent compounds *) Others

56. Implications for teaching and research A vocabulary conscious curriculum should be designed and incorporated in Japanese programs depending on the learners’ needs and language backgrounds The gap between Chinese-background learners (CBLs) and non-CBLs will be less in basic conversation and reading literary works than in reading academic texts Good curriculum for learning academic domain words is particularly desired for non-CBLs of academic Japanese Autonomous mode for learning vocabulary will be necessary particularly when the learners’ needs and language backgrounds are various

57. 8. Conclusion Limitations of the word lists Less valid in narrower domain words (2D/1D words) and less reliable in higher frequency levels  Need refining by more complete academic corpus Multi-word units not extracted Not sensitive to different usages in different domains (polysemy) Remaining issues Many transparent compounds in Japanese  What is Kanji tier? How is it related to word tier?

58. Download sites for VDRJ/JAWL Matsushita Laboratory for Language Learning http://www.wa.commufa.jp/~tatsum/English%20top_T atsu.html (Interface: English) Google it with “matsushita” and “language” 松下言語学習ラボ http://www.wa.commufa.jp/~tatsum/index.html (Interface: Japanese) Google it with “ 松下 ” and “ 言語 ”

59. Main findings VDRJ is useful for designing curriculum (material, tests etc.) AWLAD The more domains a words is shared as AW or LAD by, the more abstract the meaning of the word is. general words LW Conversation and non-academic texts contain more general words and LW AWLAD Academic texts: more AW and LAD but less LW in any academic domain proper nouns low frequency words Wikipedia: more proper nouns and low frequency words AW LAD. Newspapers and academic items of Wikipedia can be a good resource for learning AW and LAD. Natural science texts contain more academic domain words at lower frequency levels than arts and social science texts LWAW LAD Origins of academic and literary words are considerably clearly separated; 3/4 of LW originate in Japanese while 3/4 of AW and LAD originate in Chinese LAD LAD contains more Western origin words (Gairaigo)

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