Andrew Gargett

Modeling the interaction between sensory and affective meanings for detecting metaphor

Concreteness and imageability have long been held to play an important role in the meanings of figurative expressions. Recent work has implemented this idea in order to detect metaphors in natural language discourse. Yet, a relatively unexplored dimension of metaphor is the role of affective meanings. In this paper, we will show how combining concreteness, imageability and sentiment scores, as features at different linguistic levels, improves performance in such tasks as automatic detection of metaphor in discourse. By gradually refining these features through descriptive studies, we found the best performing classifier for our task to be random forests. Further refining of our classifiers for part-ofspeech, led to very promising results, with F1 scores of .744 for nouns,.799 for verbs, .811 for prepositions. We suggest that our approach works by capturing to some degree the complex interactions between external sensory information (concreteness), information about internal experience (imageability), and relatively subjective meanings (sentiment), in the use of metaphorical expressions in natural language.

Dimensions of Metaphorical Meaning

Recent work suggests that concreteness and imageability play an important role in the meanings of figurative expressions. We investigate this idea in several ways. First, we try to define more precisely the context within which a figurative expression may occur, by parsing a corpus annotated for metaphor. Next, we add both concreteness and imageability as “features” to the parsed metaphor corpus, by marking up words in this corpus using a psycholinguistic database of scores for concreteness and imageability. Finally, we carry out detailed statistical analyses of the augmented version of the original metaphor corpus, cross-matching the features of concreteness and imageability with others in the corpus such as parts of speech and dependency relations, in order to investigate in detail the use of such features in predicting whether a given expression is metaphorical or not.

Gen-Meta: Generating metaphors by combining AI and corpus-based modeling

Metaphor is important in all sorts of mundane discourse: ordinary conversation, news articles, popular novels, ad- vertisements, etc. Issues of prime human interest - such as relationships, money, disease, states of mind, passage of time - are often most economically and understandably conveyed through metaphor. This ubiquity of metaphor presents a challenge to how Artificial Intelligence (AI) systems not only understand inter-human discourse (e.g. newspaper articles), but also produce more natural-seeming language; however, most AI research on metaphor has been about its understanding rather than its generation. To redress the balance, we directly tackle the role of AI systems in communication, uniquely combining this with corpus-based results to guide output toward more natural forms of expression.

Deep generation of metaphors

We report here on progress toward a pipeline for the deep generation of metaphorical expressions in natural language. Our approach uses a combination of artificial intelligence and deep natural language generation. Metaphor is ubiquitous in forms of everyday discourse [1], [2], such as ordinary conversation, news articles, popular novels, advertisements, etc. Metaphor is an important resource for clearly and economically conveying ideas of prime human interest, such as relationships, money, disease, states of mind, passage of time. Since most Artificial Intelligence (AI) research has been about understanding rather than generating metaphorical language, such ubiquity presents a challenge to those working toward improving the ways in which AI systems understand inter-human discourse (e.g. newspaper articles, etc), or produce more natural-seeming language. Recently, there has been a renewed interest in generation, but accounts of metaphor understanding are still relatively more advanced. To redress the balance towards generation of metaphor, we directly tackle the role of AI systems in communication, uniquely combining this with corpus linguistics, deep generation and other natural language processing techniques, in order to guide output toward more natural forms of expression.