Computer Understanding of Conventional Metaphoric Language

Martin, James H.

doi:10.1207/s15516709cog1602_4

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…This account is similar to a number of recent proposals, according to which the interpretation of novel metaphors involves sense creation, but the interpretation of conventional metaphors involves sense retrieval (e.g., Blank, 1988;Blasko & Connine, 1993;Giora, 1997;Martin, 1992;Turner & Katz, 1997). In this view, sense retrieval during conventional metaphor comprehension involves accessing prestored metaphoric categories.…”

Section: Processing Implicationssupporting

confidence: 82%

Convention, Form, and Figurative Language Processing

Gentner¹,

Bowdle²

2001

Metaphor & Symbol

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There are 2 basic controversies concerning how metaphors are processed. First, are metaphoric mappings more akin to literal comparisons or to literal categorizations? And second, is metaphor comprehension indirect or direct? We believe that these controversies are more appqent than real and that a unified theoretical framework can be offered that reconciles these opposing views. The central idea is that all metaphors involve structural alignment of the target and base domains. Whether these alignments are more akin to comparisons or to categorizations, and whether they are computed directly or indirectly, depends on 2 factors: the type of representation invoked by the base term and the linguistic form of the metaphor.Metaphors establish mappings between concepts from disparate domains of knowledge. For example, in the metaphor "The mind is a computer, " an abstract entity is described in terms of a complex electronic device. It is widely believed that metaphors are a major source of conceptual change. In the field of cognitive psychology, the computer metaphor resulted in a new way of conceptualizing the mind and has inspired researchers to postulate a vast array of mental constructs. Indeed, it has recently been claimed that metaphors may be the primary (if not the only) mechanism for reasoning with abstract concepts-metaphors allow us to structure vague or ambiguous ideas in terms of more concrete realms of experience (e.g

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Section: Processing Implicationssupporting

confidence: 82%

Convention, Form, and Figurative Language Processing

Gentner¹,

Bowdle²

2001

Metaphor & Symbol

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“…Rule-based systems -some using a type of interlingua (Russell, 1976); others using complicated networks and hierarchies often referred to as metaphor maps (e.g. (Fass, 1997;Martin, 1990;Martin, 1992) -must be largely hand-coded and generally work well on an enumerable set of metaphors or in limited domains. Dictionary-based systems use existing machine-readable dictionaries and path lengths between words as one of their primary sources for metaphor processing information (e.g.…”

Section: Previous Workmentioning

confidence: 99%

Active learning for the identification of nonliteral language

Birke

Sarkar

2007

Proceedings of the Workshop on Computational Approaches to Figurative Language - FigLanguages '07

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In this paper we present an active learning approach used to create an annotated corpus of literal and nonliteral usages of verbs. The model uses nearly unsupervised word-sense disambiguation and clustering techniques. We report on experiments in which a human expert is asked to correct system predictions in different stages of learning: (i) after the last iteration when the clustering step has converged, or (ii) during each iteration of the clustering algorithm. The model obtains an f-score of 53.8% on a dataset in which literal/nonliteral usages of 25 verbs were annotated by human experts. In comparison, the same model augmented with active learning obtains 64.91%. We also measure the number of examples required when model confidence is used to select examples for human correction as compared to random selection. The results of this active learning system have been compiled into a freely available annotated corpus of literal/nonliteral usage of verbs in context.

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“…Différentes stratégies visent à repérer et/ou à représenter les expressions méta-phoriques de façon automatique (Martin 1990 ;Fass 1997 ;Kintsch 2000) ou semiautomatique (Perlerin et coll. 2002).…”

Section: Les Nucléosomes Forment Des « Perles » Le Long Du Chromosome)unclassified

“…Ainsi, les travaux de Dolan (1995) ont exploité les extensions de sens contenues dans un dictionnaire électronique afin d'identifier les emplois métaphoriques en contexte. Martin (1990) s'est intéressé à la détection de l'expression de métaphores conceptuelles dans des corpus en faisant appel aux données, en anglais, de la base de données Berkeley Metaphor List mise en ligne par Lakoff et coll. À l'inverse, les travaux de Kintsch (2000) se caractérisent par le fait qu'aucun traitement préalable n'est exigé, l'approche visant initialement à modéliser les capacités d'apprentissage du sens des mots à partir des textes.…”

Section: Les Nucléosomes Forment Des « Perles » Le Long Du Chromosome)unclassified

Approche cognitive de la traduction dans les langues de spécialité : vers une systématisation de la description de la conceptualisation métaphorique

Vandaele

Lubin

2005

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RésuméLa conceptualisation métaphorique est un processus fondamental de la pensée largement mis en oeuvre dans la modélisation scientifique, y compris en biomédecine. Pour accéder à la compréhension des textes scientifiques, il est essentiel de pouvoir appréhender les métaphores conceptuelles parcourant le domaine de spécialité envisagé. Selon notre hypothèse de travail, non seulement la conceptualisation métaphorique fonde la spécificité d’un domaine, mais encore sous-tend-elle en grande partie la terminologie et la phraséologie des langues de spécialité. En maîtriser le repérage procure donc un outil cognitif puissant permettant de fonder un grand nombre de décisions traductionnelles. Le présent article fait état d’une méthodologie autorisant la description systématique de collocations motivées par une conceptualisation métaphorique sous-jacente. La question du repérage des unités lexicales témoignant d’une conceptualisation métaphorique (« indices de conceptualisation ») sera abordée, puis une caractérisation de collocations mises en jeu autour de ces unités lexicales particulières sera proposée. La description des collocations sera envisagée à l’aide des fonctions lexicales de la Théorie Sens-Texte, lesquelles pourront être utilisées afin de faciliter le passage d’une langue à l’autre.

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Computer Understanding of Conventional Metaphoric Language

Cited by 20 publications

References 25 publications

Convention, Form, and Figurative Language Processing

Convention, Form, and Figurative Language Processing

Active learning for the identification of nonliteral language

Approche cognitive de la traduction dans les langues de spécialité : vers une systématisation de la description de la conceptualisation métaphorique

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