A Verb-centered Ontology Modelling for Text Understanding

The role of illustration is to illustrate specific words used in sentence or to express a specific situation. To automatically generate an illustration, the sentences used in contents must be understood and expressed as pictures. This requires an ontology consisting of parsing results, characters, words, affiliations, backgrounds, props, location and time. Manual correction is also required once the scene is configured. In order to improve the productivity of contents based on auto-generation of illustrations, studies for the auto-generation of illustrations are continuously performed. This study models a verb-centered ontology to be used for character-background-prop arrangement, arrangement and emotion of characters, time, weather and seasonal expression necessary for constructing illustrations of Korean traditional fairy tales. As part of the ontology used to construct the illustrations, the verb ontology not only plays a fundamental role for each verb type but also can be used for more naturally representing the behavior, placement, and emotion of characters appearing in the illustration, using different verb concepts and various relationships between concepts

The IMAGACT Visual Ontology. An Extendable Multilingual Infrastructure for the Representation of Lexical Encoding of Action

Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description

The IMAGACT Visual Ontology. An Extendable Multilingual Infrastructure for the Representation of Lexical Encoding of Action

Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description