Testing the role of metadata in metaphor identification (original) (raw)
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In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.
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Metaphor identification aims at understanding whether a given expression is used figuratively in context. However, in this paper we show how existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. We test this hypothesis in a variety of datasets and settings, and show that metaphor identification systems based on language models without complete information can be competitive with those using the full context. This is due to the construction procedures to build such datasets, which introduce unwanted biases for positive and negative classes. Finally, we test the same hypothesis on datasets that are carefully sampled from natural corpora and where this bias is not present, making these datasets more challenging and reliable.
2013 19th International Conference on Control Systems and Computer Science, 2013
ABSTRACT Because of the ubiquity of metaphors in language, metaphor processing is a very important task in the field of natural language processing. The first step towards metaphor processing, and probably the most difficult one, is metaphor detection. In the first part of this paper, we review the theoretical background for metaphors and the models and implementations that have been proposed for their detection. We then build corpora for detecting three types of metaphors: IS-A metaphors, metaphors formed with the preposition 'of' and metaphors formed with a verb. For the first two tasks, we train supervised classifiers using semantic features. For the third task, we use features commonly used in text categorization.
Cracking the Figurative Code: A Survey of Metaphor Detection Techniques
Soft Computing Research Society eBooks, 2022
Metaphor Detection is a crucial area of study in computational linguistics and natural language processing, as it enables the understanding and communication of abstract ideas through the use of concrete imagery. This survey paper aims to provide an overview of the current state-of-the-art approaches that tackle this issue and analyze trends in the domain across the years. The survey recapitulates the existing methodologies for metaphor detection, highlighting their key contributions and limitations. The methods are assigned three broad categories: feature-engineering-based, traditional deep learning-based, and transformerbased approaches. An analysis of the strengths and weaknesses of each category is showcased. Furthermore, the paper explores the annotated corpora that have been developed to facilitate the development and evaluation of metaphor detection models. By providing a comprehensive overview of the work already done and the research gaps present in preexisting literature, this survey paper hopes to help future research endeavors, and thus contribute to the advancement of metaphor detection methodologies.
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This paper describes a system that makes use of a repository of formalized frames and metaphors to automatically detect, categorize, and analyze expressions of metaphor in corpora. The output of this system can be used as a basis for making further refinements to the system , as well as supporting deep semantic analysis of metaphor expressions in corpora. This in turn provides a way to ground and test empirical conceptual metaphor theory, as well as serving as a means to gain insights into the ways conceptual metaphors are expressed in language.
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The automatic or automated metaphor identification remains a challenging problem. The methods proposed so far have been mostly developed for the English language and can be roughly divided into two groups: intended for annotated and non-annotated corpora. In addition, neural networks are used. It should also be noted that the application of recently developed methods for measuring the degree of semantic association of collocation components (T-score, MI, logDice, etc.) fails to detect metaphorical expressions. Previously, we presented a method of automated identification of metaphorical expressions (adjective + noun) for non-annotated corpora of Ukrainian prose texts, based on the analysis of dictionary definitions. This paper describes a method of automated identification of metaphors in the semantically annotated corpus of texts. This algorithm is based on the theoretical propositions and readings of metaphor within the framework of Conceptual Metaphor Theory. The methodology cont...
A Method for Linguistic Metaphor Identification
Converging Evidence in Language and Communication Research, 2010
MIPVU: A manual for identifying metaphor-related words 25 2.1 The basic procedure 25 2.2 Deciding about words: Lexical units 26 2.2.1 General guideline 27 2.2.2 Exceptions 27 2.3 Indirect use potentially explained by cross-domain mapping 32 2.3.1 Identifying contextual meanings 33 2.3.2 Deciding about more basic meanings 35 2.3.3 Deciding about sufficient distinctness 37 2.3.4 Deciding about the role of similarity 37 2.4 Direct use potentially explained by cross-domain mapping 38 2.5 Implicit meaning potentially explained by cross-domain mapping 39 2.6 Signals of potential cross-domain mappings 40 2.7 New-formations and parts that may be potentially explained by cross-domain mapping 41
MODERN APPROACHES TO AUTOMATED IDENTIFICATION OF METAPHOR
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This article discusses the existing methods and techniques of metaphor identifi cation in text corpora. It focuses on the advantages of combination of semantic and statistical approaches to metaphoric corpus annotations, the role of dictionary-based data in mechanisms of verifi cation of language media fi gurative usage and automated identifi cation of metaphors.
A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task
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In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.