Text Meaning Representation Research Articles

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

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.

Using a Natural Language Understanding System to Generate Semantic Web Content

We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over 15 years, uses a specially constructed NLP-oriented ontology and an ontological-semantic lexicon to translate English text into a custom ontology- motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an experimental Web service that monitors RSS news sources, processes the summaries of the news stories, and publishes a structured representation of the meaning of the text in the news story.

A lexicon for knowledge-based MT

In knowledge-based machine translation (KBMT), the lexicon can be specified and acquired only in close connection with the specification and acquisition of the world model (ontology) and the specification of the text meaning representation (interlingua) language. The former supplies the atoms for the specification of text meaning and provides world knowledge to support the inference processes necessary for a variety of disambiguation and meaning assignment operations. The latter is necessary for the formulation of the semantic zone of the lexicon entries, which can be viewed as containing the static building blocks of the text meaning representation. This is the view taken in the Mikrokosmos KBMT project.