We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. Our system currently only deals with cases, which we found to be quite common in edrama, in which a person is compared to, or stated to be, something non-human such as an animal, object, artefact or supernatural being. Our approach permits a limited degree of variability and extension of these metaphors. We discuss how these metaphorical utterances are recognized, how they are analysed and their affective content determined and in particular how the electronic lexical database, WordNet, and the natural language glosses of the WordNet sysnsets can be used. We also discuss how this relatively shallow approach relates in important ways to the deeper ATT-Meta theory of metaphor interpretation and to approaches to affect and emotion in metaphor theory. We finish by illustrating the approach with a number of ‘worked examples’.

I argue that the origin of the apparent systematicity found in families of related metaphors lies not in ontologically-rich skeletal source domain schemas with slots that map to target domain correspondents. This suggests a rigidity which is inappropriate, and misses significant cross-metaphor generalisations. Instead, I claim that metaphors utilise just a few core source-target correspondences. Users can extend and elaborate upon these, and by doing so give the impression of a systematic exploitation of a domain, by incorporating into the utterance any aspect of the user's encyclopaedic knowledge that can be linked to the core correspondences. However, this linkage is subject to the constraint that any conclusions then drawn about the source domain meaning must ultimately be grounded in specific types of information or meaning that transfer invariantly between source and target as adjuncts to the core correspondences. It is in these invariant mappings that systematicity is to be found.

We present a computationally-oriented formal semantic framework to address the interpretation of metaphorical text. The framework builds on two main features: inference on source domain terms and a set of special mappings. Such mappings are domain-independent and are adjuncts to any conceptual metaphor.

We report work on adding affect-detection to an existing e-drama programme, a text-based software system for (human) dramatic improvisation in simple virtual scenarios, for use primarily in learning contexts. The system allows a human director to monitor improvisations and make interventions, for instance in reaction to excessive, insufficient or inappropriate emotions in the characters' speeches. Within an endeavour to partially automate directors' functions, and to allow for automated affective bit part characters, we have developed an affect-detection module. It is aimed at detecting affective aspect (concerning emotions, moods, rudeness, value judgments, etc.) of human-controlled characters' textual 'speeches'. The work also accompanies basic research into how affect is conveyed linguistically. A distinctive feature of the project is a focus on the metaphorical ways in which affect is conveyed. The project addresses the special issue themes such as making interactive narrative learning environments more usable, building them, and supporting reflection on narrative construction.

We discuss an aspect of an affect-detection system used in e-drama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. We discuss how these metaphorical utterances are recognized and how they are analysed and their affective content determined.

We aim to address two complementary deficiencies in Natural Language Processing (NLP) research: (i) Despite the importance and prevalence of metaphor across many discourse genres, and metaphor's many functions, applied NLP has mostly not addressed metaphor understanding. But, conversely, (ii) difficult issues in metaphor understanding have hindered large-scale application, extensive empirical evaluation, and the handling of the true breadth of metaphor types and interactions with other language phenomena. In this paper, abstracted from a recent grant proposal, a new avenue for addressing both deficiencies and for inspiring new basic research on metaphor is investigated: namely, placing metaphor research within the "Recognizing Textual Entailment" (RTE) task framework for evaluation of semantic processing systems.

We demonstrate one aspect of an affect-extraction system for use in intelligent conversational agents. This aspect performs a degree of affective interpretation of some types of metaphorical utterance.

We describe a computational treatment of certain sorts of affect-conveying metaphorical utterances. This is part of an affect detection system used by intelligent conversational agents (ICAs) operating in an edrama system.

In this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. Such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. We also show a way of embedding the invariant mappings in a semantic framework.

In this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. Such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. Furthermore, we show the role that these invariant mappings play in a discourse framework for metaphor interpretation.

Enabling machines to understand emotions and feelings of the human users in their natural language textual input during interaction is a challenging issue in Human Computing. Our work presented here has tried to make our contribution toward such machine automation. We report work on adding affect-detection to an existing e-drama program, a text-based software system for dramatic improvisation in simple virtual scenarios, for use primarily in learning contexts. The system allows a human director to monitor improvisations and make interventions, for instance in reaction to excessive, insufficient or inappropriate emotions in the characters’ speeches. Within an endeavour to partially automate directors’ functions, and to allow for automated affective bit-part characters, we have developed an affect-detection module. It is aimed at detecting affective aspects (concerning emotions, moods, value judgments, etc.) of human-controlled characters’ textual “speeches”. The work also accompanies basic research into how affect is conveyed linguistically. A distinctive feature of the project is a focus on the metaphorical ways in which affect is conveyed. Moreover, we have also introduced how the detected affective states activate the animation engine to produce gestures for human-controlled characters. The description of our approach in this paper is taken in part from our previous publications [1, 2] with new contributions mainly on metaphorical language processing (practically and theoretically), 3D emotional animation generation and user testing evaluation. Finally, Our work on affect detection in open-ended improvisational text contributes to the development of automatic understanding of human language and emotion. The generation of emotional believable animations based on detected affective states and the production of appropriate responses for the automated affective bit-part character based on the detection of affect contribute greatly to the ease and innovative user interface in e-drama, which leads to high-level user engagement and enjoyment.

In this paper we provide a formalization of a set of default rules that we claim are required for the transfer of information such as causation, event rate and duration in the interpretation of metaphor. Such rules are domain-independent and are identified as invariant adjuncts to any conceptual metaphor. Furthermore, we show the role that these invariant mappings play in a semantic framework for metaphor interpretation.

We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. We discuss how these metaphorical utterances are recognized and how they are analysed and their affective content determined.

We report progress on adding affect-detection to a program for virtual dramatic improvisation, monitored by a human director. To aid the director, we have partially implemented emotion detection. within users’ text input. The affect-detection module has been used to help develop an automated virtual actor. The work involves basic research into how affect is conveyed through metaphor and contributes to the conference themes such as building improvisational intelligent virtual agents for interactive narrative environments.

We report work in progress on adding affect-detection to an existing program for virtual dramatic improvisation, monitored by a human director. To partially automate the directors' functions, we have partially implemented the detection of emotions, etc. in users' text input, by means of pattern-matching, robust parsing and some semantic analysis. The work also involves basic research into how affect is conveyed by metaphor.

This paper discusses the nature of the metaphorical transfer from the source domain to the target domain. More specifically, it explores the question as to how the mapping links between features of the source and the target are created. It is argued that, for many metaphors, it is incorrect to assume that all the elements of the source domain map to elements of the target domain, and that a much more economical set of mappings should be used instead.Este trabalho discute a natureza da transferência metafórica entre domínio fonte e domínio alvo. Argumenta-se que, para grande parte das metáforas, não seria correto afirmar que todos os elementos do domínio alvo sejam mapeados para os elementos do domínio fonte, e que um conjunto bem mais econômico de mapeamentos é utilizado.