1. October 2003 Yu Sun (PAMIR) World Knowledge Enhancement Using Tools of Precisiated Natural Language Yu Sun (PAMIR) Supervisors Prof. F. Karray Prof. O. Basir

2. October 2003Yu Sun (PAMIR) Previous Works on Term Related Unified Semantic Tree (TRUST) Previous work on TRUST is based on pure statistical approachTRUST Disadvantages: Huge dimensions lead to complexity Estimated parameters are based on insufficient data and therefore imprecise

3. October 2003Yu Sun (PAMIR) Previous Works (cont) Questions left: How to impart human Knowledge into the statistical approach How to use such human Knowledge to tune the TRUST

4. October 2003Yu Sun (PAMIR) How to Tackle the Issues The novel approach is implemented through: Epistemic Lexicon (EL) Precisiated Natural Language (PNL)

5. October 2003Yu Sun (PAMIR) Epistemic Lexicon (EL) A lexine uses granule fuzzy technique to define a term and its attributes, its relations to other lexine terms. lexine i lexine j r ij

6. October 2003Yu Sun (PAMIR) Examples of EL EL is a static representation of knowledge Buyer: institute (company, bank) / very possible, management-staff / possible, government / not very possible Seller: institute (company, bank) / very possible, management-staff / possible, government / not very possible,

7. October 2003Yu Sun (PAMIR) Precisiated Natural Language ( PNL ) EL is a static dictionary. There need generalize constraints to compensate the insufficiency. PNL is a sub-language of precisiable propositions in NL which is equipped with a dictionary (EL) defined by domain experts and Generalized Constraint (rules of deduction) GC dynamically applies EL on NLU

8. October 2003Yu Sun (PAMIR) Examples of GC Generalized Constraint in PNL is used to enhance the static EL through rules Any lexine in EL (word) is bi-sense-directed: belongs to one of two directions: in/out or up/down dependent on its context; One lexine might belong to multi concepts (classes) [Self]; If Positive(+, such as agree, pro) meets Negative(-, such as against, con), the result is Negative(-) [combination]; Lexine ’ s relationship can be inherited by its attributes. For instance, Institute has the following attributes: management- staff, performance, legal, scandal (which are defined by the original EL). If action Against(Institute), then Against(Institute.management-people, Institute.performance, Institute.legal, Institute.scandal). And vice versa [Inheritance].

9. October 2003Yu Sun (PAMIR) Example of GC Enhancing NLU This example shows how GC enhances the system to correctly understand NL: “Beech-Nut Corp. damaged its image over the sale of apple juice that turned out to be water” EL: Performance-up(sale) [default]; Against(damage); Company(image, sale) GC: Against(damage)  Against(company.image)  Against(company)  Against(company.sale)  Against(Performance-up(sale))  Performance- down(sale) [inferred]

10. October 2003Yu Sun (PAMIR) Experiment Pre-processing (manual annotation) 5 documents from WSJ with unique-topic: “company-takeover” Epistemic Lexicon: including 35 concepts(classes), such as action-in, performance-up, buyer, seller, etc. Generalized Constraints: rules governing combinational operations of concepts.

11. October 2003Yu Sun (PAMIR) Results of Applying PNL Experimental results showed the tuned TRUST much closer to human common sense Enlarged and Adjusted EL after tuning TRUST Buyer: action-in / very possible, agree/very possible, management-staff/not very possible legal / not very possible Seller: action-out / very possible, legal / possible management-staff/ possible sale / hard to tell

12. October 2003Yu Sun (PAMIR) To identify the semantic meaning of a given term (word sense disambiguation): Possible Applications “ The juice scandal forced Beech-Nut to pay a $ 2.2 million fine and $ 7.5 million to settle a lawsuit” Problem: Beech-Nut may be buyer or seller Analysis: Against(scandal)  closer to Seller; Against(fine)  closer to Seller; Legal(lawsuit)  closer to Seller; Solution: Beech-Nut is a Seller

13. October 2003Yu Sun (PAMIR) Future Possible Applications Work as an assistant to a semantic parser: For instance: First Pennsylvania had agreed to be acquired by Marine Midland in several month ago…. Midland decides to step out the acquisition and it starts a lawsuit. (lawsuit is closer to seller than to buyer) Work as an assistant to a syntactic parser: For instance: Ralston Co. agreed to buy Beech-Nut Nutrition Corp. with a favorite offer. anaphor resolution pp attachment

14. October 2003Yu Sun (PAMIR) Conclusions Present a novel approach imparting human knowledge into statistical approach; The preliminary experiment shows PNL and EL improve the statistical approach; The experiment uses unique-topic documents and in future, research work will expand to more complex documents.

15. October 2003Yu Sun (PAMIR) Publications Submitted to Journal of Fuzzy Sets and Systems

16. October 2003Yu Sun (PAMIR) Overview of Architecture

17. October 2003Yu Sun (PAMIR) Comparing Procedure of Untuned TRUST and Tuned TRUST

18. October 2003Yu Sun (PAMIR)

19. October 2003Yu Sun (PAMIR)