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Ontology-based view of natural language meaning: the case of humor detection

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Abstract

This paper deals with computational detection of humor. It assumes that computational humor is an useful task for any number of reasons and in many applications. Besides these applications, it also shows that recognition of humor is a perfect test platform for an advanced level of language understanding by a computer. It discusses the computational linguistic/semantic preconditions for computational humor and an ontological semantic approach to the task of humor detection, based on direct and comprehensive access to meaning rather than on trying to guess it with statistical-cum-syntactical keyword methods. The paper is informed by the experience of designing and implementing a humor detection model, whose decent success rate confirmed some of the assumptions while its flaws made other ideas prominent, including the necessity of full text comprehension. The bulk of the paper explains how the comprehensive meaning access technology makes it possible for unstructured natural language text to be automatically translated into the ontologically defined text meaning representations that can be used then to detect humor in them, if any, automatically. This part is informed by the experience, subsequent to humor detection, of designing, implementing, and testing an ontological semantic text analyzer that takes an English sentence as input and outputs its text meaning representation (TMR). Every procedure mentioned in the paper has either been implemented or proven to be implementable within the approach.

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Notes

  1. It would be distracting from the immediate goals of this paper to elaborate here on the crucial differences between the ontology described here and designed to represent meaning for computational applications, on the one hand, and the prevalent controlled-vocabulary-type ontologies proliferating recently in the government and industry and designed for human use, on the other (see Obrst (2007) for a good survey of the latter and Hempelmann & Raskin (2008) for the comparison of the semantic ontology to the others).

  2. The shown list has been enriched in the current implementation of OST, but since facets do not contribute much to the joke analysis in this paper, the list is left as it was first introduced.

  3. The detailed TMRs are not shown here, but screen captures of the proprietary software can be available with a signed NDA.

  4. Simple ambiguity should not be confused with script opposition. A related discussion on common puns can be found in Hempelmann 2003.

  5. As pointed out by C.F. Hempelmann 2008, personal communication.

  6. SO setup:punchline was suggested by C.F. Hempelmann 2008, personal communication.

  7. It should be noted that while it may seem that a large amount of common sense reasoning is involved here, all of it is contained in the appropriate ontological concepts. While possibly surprising to mainstream, i.e., non semantical NLP researchers, it is perfectly feasible,, as the practical applications of the technology on other domains reveal.

  8. Notice that A hostess showed (his) seat (to a man) introduces a different meaning: a hostess could point his seat to the man or explain to him where his seat is without walking him there.

  9. In reality, a shortcut is used, and a knowledge base of previously TMR’ed texts is consulted to determine the most suitable property.

  10. No phrasal senses, such as approach-about, qualify here syntactically.

  11. Strictly speaking, this TMR should activate another property speech act and fill it: speech act: interrogative. This property is rarely used in the implementations of OST because most of the expository texts in the applications are monotonously “assertive” on it.

  12. The fillers of the desirability and intentionality are high and medium, which, incidentally, are classical examples of fuzzy sets (Zadeh 1965), memberships of which are expressed with the use of facets.

  13. Property of speech act with the filler “imperative” could be added to TMRs 12, 13 and 14.

  14. Cause here should not be looked as the causal property, but rather the event.

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Acknowledgments

The author greatly appreciates Victor Raskin’s comments and suggestions on this work. The ontological semantics research and implementation upon which the joke analysis draws were done in close collaboration with Victor Raskin and Christian Hempelmann as well as with the assistance of the OnSe Group at RiverGlass Inc.

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  1. RiverGlass Inc., Champaign, IL, USA

    Julia M. Taylor

  2. Purdue University, West Lafayette, IN, USA

    Julia M. Taylor

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Correspondence to Julia M. Taylor.

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Taylor, J.M. Ontology-based view of natural language meaning: the case of humor detection. J Ambient Intell Human Comput 1, 221–234 (2010). https://doi.org/10.1007/s12652-010-0014-2

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