1. Web-Based Concordancer to Learn Usage of English Expressions Takashi Yamanoue Kyushu Institute of Technology, Japan Toshiro Minami Kyushu Institute of Information Sciences & Kyushu University, Japan Ian Ruxton Kyushu Institute of Technology, Japan ICITA2002@Bathurst (02.11.25-28)

2. Contents  Motivation  System Design and Implementation  Examples and Experiments for Evaluation  Related Work  Concluding Remarks

3.  Difficulties in writing in English Is it really used? ? ? Spelling → spell checker Grammar → grammar checker Usage → ? Motivation

4. Help for Usage  Corpus linguistics  Time-consuming  Needs hard work in order to make a good corpus  Copyright problem  (often) Outdated from the beginning  Tools are mainly for experts  Difficult to use for ordinary learners

5. A Solution  Use of “ Web-Corpus ” = Using Web Documents as a Corpus  Maintenance free: Exists as it is  Always new, reflects current status of languages  A lot of applications/services are available on the Internet

6. System Design and Implementation （ WebLEAP ）

7. Features  With WebLEAP(Web Language Evaluation Assistant Program), we can get some information for:  comparing expressions,  deciding if the expression is right or wrong, and  finding out wrong parts.

8. System Organization WebLEAP

9.  See/watch the TV/movie “the TV” “see the TV” “watch the TV” 216,107 873* 1,593 “the movie” “see the movie” “watch the movie” 472,776 13,638 6,666* common. Not common Examples and Experiments for Evaluation

10. Experiments  Person ’ s Name Place Names “Bertrand Russell” “Burtrand Russell” 12,575 3 Not only for Historical names “Jenolan caves” “Genolan caves” 992 2

11.  Choose one phrase or word that should be corrected.  Successful : 13/20  Insufficient information: 2/20  Failure: 5/20 TOEIC (Test of English for International Communication)

12. Only 9.4% of the testees gave the correct answer Successful Example:

13. Failure Example:

14. Comparison with others  WebLEAP detects 13(+2) /20  Checker1 detects 7 /20  Checker2 detects 4 /20  WebLEAP shows not only places but also popularity(degree).

15. WebLEAP Experimental Class  11 students  Writing essays for 30 min.  2 classes with same members  Compare the numbers of errors

16. Results Student Essay1Essay2 errorswordsdensityerrorswordsdensity 15539%4488%↓ 294321%63219%↓ 353116%43611%↓ 443412%73321% 553415%64214%↓ 653415%43910%↓ 753913%84020% 865411%1119%↓ 93359%3408%↓ 1043412%53216% 1143312%73222%

17. Evaluation  More than half of the students got the decrease of the error densities.  (Current) WebLEAP is not good enough for beginners of English.  Thus for most of them are in the “ TGR(Teachers Guidance Recommended) ” level.  All the students enjoyed using WebLEAP.  WebLEAP makes us think and interpret the expressions we use. → Educational Effect

18.  Satoh ’ s system … WWW, KWIC  WordSmith Tools … for experts  TXTANA … for experts  SARA … for research purpose  SUIKO … detect wrong sentences  The Writer ’ s Assistant … idea organizer Related Work

19. Sato ’ s System: Input

20. Sato ’ s System: Results

21.  WebLEAP:  is a tool for helping with writing.  gives us information To know, is it really used?  is not for beginners.  Need skill to interpret the given results.  is such a system that users are excited about using it. Concluding Remarks

22.  Integrate with Web-based and other types of services  Monitor user ’ s behavior and get tips for improvement  Assist collaborative writing Further Research Topics

23. Thank you for Listening!