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World Knowledge Enhancement Using Tools of Precisiated Natural Language Yu Sun (PAMIR) Supervisors Prof. F. Karray Prof. O. Basir - PowerPoint PPT Presentation


World Knowledge Enhancement Using Tools of Precisiated Natural Language Yu Sun (PAMIR) Supervisors Prof. F. Karray Prof. O. Basir. Previous Works on Term Related Unified Semantic Tree (TRUST). Previous work on TRUST is based on pure statistical approach Disadvantages:

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

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World Knowledge Enhancement Using Tools of Precisiated Natural LanguageYu Sun (PAMIR)SupervisorsProf. F. KarrayProf. O. Basir

Yu Sun (PAMIR)


Previous Works on Term Related Unified Semantic Tree (TRUST)

  • Previous work on TRUST is based on pure statistical approach

  • Disadvantages:

    • Huge dimensions lead to complexity

    • Estimated parameters are based on insufficient data and therefore imprecise

Yu 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

Yu Sun (PAMIR)


How to Tackle the Issues

The novel approach is implemented through:

  • Epistemic Lexicon (EL)

  • Precisiated Natural Language (PNL)

Yu Sun (PAMIR)


A lexine uses granule fuzzy technique to define a term and its attributes, its relations to other lexine terms.

Epistemic Lexicon (EL)

rij

lexinej

lexinei

Yu Sun (PAMIR)


Examples of EL

  • EL is astatic 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,

Yu Sun (PAMIR)


PrecisiatedNaturalLanguage (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

Yu 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 asagree, pro) meets Negative(-, such asagainst, 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].

Yu 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]

Yu 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.

Yu 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

Yu Sun (PAMIR)


Possible Applications

  • To identify the semantic meaning of a given term (word sense disambiguation):

“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

Yu 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 itstarts a lawsuit. (lawsuit is closer to seller than to buyer)

  • Work as an assistant to a syntactic parser:

    For instance:

    Ralston Co. agreed to buyBeech-Nut Nutrition Corp. with a favorite offer.

anaphor resolution

ppattachment

Yu 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.

Yu Sun (PAMIR)


Publications

  • Submitted to Journal of Fuzzy Sets and Systems

Yu Sun (PAMIR)


Overview of Architecture

Yu Sun (PAMIR)


Comparing Procedure of Untuned TRUST and Tuned TRUST

Yu Sun (PAMIR)


Yu Sun (PAMIR)


Yu Sun (PAMIR)


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