Computational Metaphor Identification: A Method for Identifying Conceptual Metaphors in Written Text (original) (raw)
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MODERN APPROACHES TO AUTOMATED IDENTIFICATION OF METAPHOR
2019
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.
Automatic Identification of Conceptual Metaphors With Limited Knowledge
Full natural language understanding requires identifying and analyzing the meanings of metaphors, which are ubiquitous in both text and speech. Over the last thirty years, linguistic metaphors have been shown to be based on more general conceptual metaphors, partial semantic mappings between disparate conceptual domains. Though some achievements have been made in identifying linguistic metaphors over the last decade or so, little work has been done to date on automatically identifying conceptual metaphors. This paper describes research on identifying conceptual metaphors based on corpus data. Our method uses as little background knowledge as possible, to ease transfer to new languages and to minimize any bias introduced by the knowledge base construction process. The method relies on general heuristics for identifying linguistic metaphors and statistical clustering (guided by Wordnet) to form conceptual metaphor candidates. Human experiments show the system effectively finds meaningful conceptual metaphors.
Evaluating the Premises and Results of Four Metaphor Identification Systems
Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2013)
This study first examines the implicit and explicit premises of four systems for identifying metaphoric utterances from unannotated input text. All four systems are then evaluated on a common data set in order to see which premises are most successful. The goal is to see if these systems can find metaphors in a corpus that is mostly non-metaphoric without over-identifying literal and humorous utterances as metaphors. Three of the systems are distributional semantic systems, including a source-target mapping method [1-4]; a word abstractness measurement method [5], [6, 7]; and a semantic similarity measurement method [8, 9]. The fourth is a knowledge-based system which uses a domain interaction method based on the SUMO ontology [10, 11], implementing the hypothesis that metaphor is a product of the interactions among all of the concepts represented in an utterance [12, 13].
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
Metaphor Identification in Large Texts Corpora
PLoS ONE, 2013
Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms' performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.
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.
SupervisedWord-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples
Proceedings of the Third Workshop on Metaphor in NLP (at NAACL 2015), 2015
We present a supervised machine learning system for word-level classification of all content words in a running text as being metaphorical or non-metaphorical. The system provides a substantial improvement upon a previously published baseline, using re-weighting of the training examples and using features derived from a concreteness database. We observe that while the first manipulation was very effective, the second was only slightly so. Possible reasons for these observations are discussed.