Metaphor Interpretation Using Paraphrases Extracted from the Web (original) (raw)

Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks

Proceedings of the Workshop on Figurative Language Processing, 2018

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75% accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.

Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts

Cornell University - arXiv, 2022

This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models. Unlike similar studies, the proposed algorithm does not only focus on verbs but also on nouns and adjectives. Despite the fact that the transfer rate for the former is the highest (56%), the transfer of the latter is feasible (24% and 31%). Human evaluation showed that our system-generated metaphors are considered more creative and metaphorical than human-generated ones while when using our transferred metaphors for data augmentation improves the state of the art in metaphorical sentence classification by 3% in F1.

Interpretation of Metaphors with Perceptual Features Using WordNet

Engineering Psychology and Cognitive …, 2011

Metaphors based on perceptual similarity play a key role in stimulating creativity. Here, we present a metaphor interpretation tool using features of source and target to generate perceptual metaphors which might be conceptually very different, thereby generating new interpretations from familiar concepts.

MetaNet: Deep semantic automatic metaphor analysis

Proceedings of the 3rd Workshop on Metaphor in NLP, NAACL HLT 2015

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.

The Effect of Context on Metaphor Paraphrase Aptness Judgments

Proceedings of the 13th International Conference on Computational Semantics - Long Papers

We conduct two experiments to study the effect of context on metaphor paraphrase aptness judgments. The first is an AMT crowd source task in which speakers rank metaphorparaphrase candidate sentence pairs in short document contexts for paraphrase aptness. In the second we train a composite DNN to predict these human judgments, first in binary classifier mode, and then as gradient ratings. We found that for both mean human judgments and our DNN's predictions, adding document context compresses the aptness scores towards the center of the scale, raising low outof-context ratings and decreasing high out-ofcontext scores. We offer a provisional explanation for this compression effect.

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.

Introducing the LCC Metaphor Datasets

2016

In this work, we present the Language Computer Corporation (LCC) annotated metaphor datasets, which represent the largest and most comprehensive resource for metaphor research to date. These datasets were produced over the course of three years by a staff of nine annotators working in four languages (English, Spanish, Russian, and Farsi). As part of these datasets, we provide (1) metaphoricity ratings for within-sentence word pairs on a four-point scale, (2) scored links to our repository of 114 source concept domains and 32 target concept domains, and (3) ratings for the affective polarity and intensity of each pair. Altogether, we provide 188,741 annotations in English (for 80,100 pairs), 159,915 annotations in Spanish (for 63,188 pairs), 99,740 annotations in Russian (for 44,632 pairs), and 137,186 annotations in Farsi (for 57,239 pairs). In addition, we are providing a large set of likely metaphors which have been independently extracted by our two state-of-the-art metaphor dete...

Beyond Selectional Preference: Automatic Metaphor Recognition Based on Semantic Relation Patterns

This paper analyzes the limitations of selectional-preference based metaphor recognition, and proposes a new model for metaphor recognition, using Chinese subject-predicate construction as illustration. After showing with experiments that selectional-preference based metaphor recognition has difficulty in recognizing conventional metaphors and literal expressions with low frequency, the paper presents a metaphor recognition model which is based on Semantic Relation Pattern, a distribution pattern integrating six types of semantic relations between a subject head and other subject heads within a subject-predicate cluster, and employs a SVM classifier for metaphor recognition. Contrastive Experiments show that the proposed model achieves an F1 of 89% in metaphor recognition, about 37% higher than the selectional-preference based model. Further analysis shows that the proposed model is able to account for lexicalized metaphors, truth-condition literality and other types of literality and metaphor failed in selectional-preference based models. More importantly, the proposed model possesses the ability to generalize to unknown predicate heads. Theoretically, the semantic-relation-pattern model can also be applied for metaphor recognition in other endocentric constructions such as verb-objects and adjective-nouns.

Robust Extraction of Metaphors from Novel Data

2013

This article describes our novel approach to the automated detection and analysis of metaphors in text. We employ robust, quantitative language processing to implement a system prototype combined with sound social science methods for validation. We show results in 4 different languages and discuss how our methods are a significant step forward from previously established techniques of metaphor identification. We use Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline.

Robust Extraction of Metaphor from Novel Data

2013

This article describes our novel approach to the automated detection and analysis of metaphors in text. We employ robust, quantitative language processing to implement a system prototype combined with sound social science methods for validation. We show results in 4 different languages and discuss how our methods are a significant step forward from previously established techniques of metaphor identification. We use Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline.