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Exploring Syllogisms and Natural Language Processing Through CLIPS Implementation

This paper by John Swartz, presented by Stan Lovric, delves into symbolic logic and the implementation of a syllogism proving system using CLIPS. Based on Lewis Carroll's work from 1896, the system addresses two syllogism types—verifying conclusions and deriving conclusions from premises. The study highlights the integration of natural language processing, emphasizing layers of abstraction for system complexity. The results showcase valid syllogisms and offer insight into NLP solutions, despite a lack of detailed outcomes on the effort.

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Exploring Syllogisms and Natural Language Processing Through CLIPS Implementation

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  1. Victorian Symbolic Logic Paper by John Swartz Presentation by Stan Lovric

  2. Problem Definition Syllogism: Some holidays are rainy. Rainy days are tiresome. Some holidays are tiresome. • CLIPS implementation of a syllogism proving system • Based on “Symbolic logic” published by Lewis Carroll in 1896. • The system solves two kinds of syllogisms. (1) given two premises and a conclusion, it checks the correctness of the conclusion and (2) given only two premises, it derives a conclusion if there is one.

  3. Motivation • Feasibility study of natural language processing system (NLP). • Use layers of abstraction to implement a complex system. • Begin with CLIPS as the lowest level of abstraction. • To solve the problem, the software engineer must implement the intervening abstraction layers.

  4. Program Output • CLIPS> (run) • input line value is: >>>> some holidays are rainy: rainy days are tiresome. some holidays are tiresome. • The subject syllogism is true!!! and is of type >>> ([figureTwoA]) • input line value is: >>>> no m' are x: some m' are y'. • The subject syllogism implies the following conclusion >>> some x' are y' • input line value is: >>>> all x are m: no y are m'. no y are x'. • The subject syllogism is false!!! () • input line value is: >>>> EOF • Thus endeth the input file • CLIPS> (exit)

  5. Conclusion • The author wanted to show a possible path to the general solution of the NLP problem. • Interesting approach, but no information about the outcome of the effort.

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