The Development of E2T and T2E Active Reading via Web

1. The Development of E2T and T2E Active Reading via Web Asanee Kawtrakul and Teams Kasetsart University, Bangkok, Thailand ak@vivaldi.cpe.ku.ac.th Fifth Agricultural Ontology Service (AOS) Workshop 29 April 2004, Beijing, China

2. Outline • Motivation • Objectives • System Overview • Methodologies • Example • Conclusion and Future work

3. Acknowledgement • KURDI Kasetsart University Research and Development Institute

4. Collaboration • Library Institute of Kasetsart University • Providing thesaurus and Agricultural Corpus

5. Motivation • Valued data scattering throughout the organization in multi-language • Good Information collected by many individuals in unstructured format • Digested information gives quicker decision-making

6. Proposed project • Summarization • From unstructured to structured format • Only the gist of information • Translation • From English to Thai (E2T) • Thai to English (T2E)

7. Objectives • To develop a system for summarizing and translating the agricultural information from English to Thai using statistical and frame-based approach (E2T) • To support the development of information discovery and web-based information exchange in the agricultural domain(T2E)

8. E2T

9. Summarization (Input) Let us focus on Canada’s agricultural products. In 1998, there were 1,216 registered commercial egg producers in Canada. Ontario produced 39.8% of all eggs in Canada, Quebec was second with 16.6%. The western provinces have a combined egg production of 35.6% and the eastern provinces have a combined production of 8.0%. With a courtesy of Agriculture and Agri-Food Canada, http://www.agr.ca/cb

10. Summarization (Cube)

11. Other Output

12. Some related works • Frame • Knowledge representation in form of slot and filler • Consisting of attributes and their values Attributes Values

13. Methodologies • Integration of NLP techniques and data cube structure • Gist of information extracted and summarized by frames and then translated into the target language • Data cube structure supporting efficient data access management and powerful decision making • Focusing on the case • Agricultural summary articles which have merely similar structure

14. Why needs NLP techniques? • NP Analysis • To extract the name entity for activating a frame • To enhance the performance of indexing • Word sense Disambiguation • Pound • The basic monetary unit of the United Kingdom • Unit of mass and weight

15. Internet SummarizationModule TranslationModule GatheringModule Indexingand ClusteringModule Document Database Data Cube System Overview GraphicalUser Interface

16. Internet Preprocessing Web Robot AgriculturalPapers’Abstracts Document Database Gathering Module

17. Lexical TokenIdentification WeightComputation Multi-levelIndexing(Word, Phrase,and Concept) PhraseExtraction DocumentClassification (Statistical Method) Documents Clusters ofDocuments Indexing and Clustering Module

18. FrameGeneration SentenceFiltering ShallowParsing Document Frames TranslationTemplates(Depending onContent’s Domain) Knowledge Base: Frame, Thesaurus Data Cube Summarization Module SentenceStructures

19. Summarization (Input) Let us focus on Canada’s agricultural products. In 1998, there were 1,216 registered commercial egg producers in Canada. Ontario produced 39.8% of all eggs in Canada, Quebec was second with 16.6%. The western provinces have a combined egg production of 35.6% and the eastern provinces have a combined production of 8.0%. With a courtesy of Agriculture and Agri-Food Canada, http://www.agr.ca/cb

20. Let us focus on Canada’s agriculturalproducts. In 1998, there were 1,216 registeredcommercial egg producers in Canada. Ontario produced 39.8% of all eggsin Canada. Quebec was second with 16.6% The western provinces have a combinedegg production of 35.6%. The eastern provinces have a combinedproduction of 8.0%. Summarization (Filtering) Let us focus on Canada’s agriculturalproducts. In1998, there were 1,216 registeredcommercial egg producers in Canada. Ontario produced39.8% of all eggsin Canada. Quebec was second with 16.6% The western provinces have a combinedeggproduction of 35.6%. The eastern provinces have a combinedproduction of 8.0%.

21. Summarization (Templates)

22. Summarization (Frames)

23. Summarization (Cube)

24. Translationand MeasurementUnit Conversion QueryProcessing User’s Query Biligual Dictionary and Thesaurus Data Cube Translation Module VisualizationTool

25. Translation Result

26. Web-based User Interface • To make inquiries about the history of agricultural products’ price, including their chronological, statistical data

27. Output

28. Current State E2T: the system • Parser: Shallow parsing • English to Thai • Summarization and Translation: Frame-based • Text to relational database

29. S np vp SL Analysis adj n np big dog n v Transfer cat loves S adj np vp small n adj v np สุนัข ใหญ่ n adj รัก TL Generation แมว เล็ก Parser Big dog loves small cat. สุนัข ใหญ่ รัก แมว เล็ก /sulnakh yail rakh määwm lekh/

30. T2E

31. Input and Output • Input characteristics (SL) • Web pages must be of ‘html’ file only • Web pages displayed in Thai • Output characteristics (TL) • The system will display output in English by popping up the new window

32. Why Translate only Table? • From the survey, the agricultural web pages could be divided into 3 types • Full text • Tables with contexts • Tables only (approx. 50%)

33. Table Characteristics (cnt.) Unit Pure Texts Heading (Outside Table) Numeric

34. Table Characteristics (cnt.) Unit outside table Unit Inside table

35. Input Format Example • Input as Frame format Department of Internal Trade (DIT) Office of Agriculture Economics (OAE)

36. Tables only Picture Bullet Agricultural Economics News

37. Dictionary & Grammar Rules ConversionTable Pages Output TableAnalysis Chunk-level Translation UnitConversion OutputGeneration System overview

38. Input Webpage HTML File Web Robot Internet

39. Html Parser Table Analysis HTML File Tag with position anchor Text with position anchor

40. Position Anchor (Table Analysis) • Using letter to stand for the data’s position in each cell of table • T stands for ‘table’ • R stands for ‘row’ • C stands for ‘column’

41. Keyword Definition Example(Table Analysis) The result will be: T1R1C1 ^ ข้าว T1R1C2 ^ 1999 T1R1C3 ^ 2000 T1R2C1 ^ ประเทศไทย T1R2C2 ^ 24,245 T1R2C3 ^ 28,356 T2R1C1 ^ ข้าวโพด T2R1C2 ^ 1999 T2R2C1 ^ ประเทศไทย T2R2C2 ^ 2,172,000

42. Phrase Chunker & NE Extraction Dictionary & Grammar Rules Chunk-level Translation Translated File Text with Keyword

43. np vp n v n Phrase Chunker (cnt.)(Chunk level Translation) rules 1: np  n+ vp vp  aux? v n ราคา นำเข้า สินค้า 1: 2: 3:

44. Phrase Chunker (Chunk level Translation)

45. Chunk level Translation (cnt.) • Handle with Name Entity! • NE cannot be word-by-word translated e.g. กองควบคุมพืชและวัสดุการเกษตร • Chunker  AGRICULTURAL PLANT AND MATERIAL CONTROL DIVISION • NE Extraction  AGRICULTURAL REGULATORY DIVISION

46. Table Characteristics (Unit Conversion) Unit outside table 1 2 Unit Inside table

47. Unit conversion (cnt.)

48. np vp n v n Sentence Generation rules 1: np  n+ vp vp  aux? v+ n ราคา นำเข้า สินค้า 1: 2: 3:

49. Sentence Generation (cnt.) [NP ราคา[vp นำเข้า สินค้า]] Transfer rules ThaiEnglish np  n+ vp np  adjp n+ vp  v+ n adjp  adj* | np [NP [np สินค้า นำเข้า]ราคา] [NP [np goods importing]ราคา] [NP [np goods importing] price]

50. Result Active Reading