Metaphorical Language Change Is Self-Organized Criticality
- URL: http://arxiv.org/abs/2211.10709v1
- Date: Sat, 19 Nov 2022 14:38:38 GMT
- Title: Metaphorical Language Change Is Self-Organized Criticality
- Abstract summary: The paper argues that metaphorical language change qualifies as a self-organized criticality state.
It provides a statistical profile of metaphorical constructions and intrinsic generative rules with antecedent conditions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One way to resolve the actuation problem of metaphorical language change is
to provide a statistical profile of metaphorical constructions and generative
rules with antecedent conditions. Based on arguments from the view of language
as complex systems and the dynamic view of metaphor, this paper argues that
metaphorical language change qualifies as a self-organized criticality state
and the linguistic expressions of a metaphor can be profiled as a fractal with
spatio-temporal correlations. Synchronously, these metaphorical expressions
self-organize into a self-similar, scale-invariant fractal that follows a
power-law distribution; temporally, long range inter-dependence constrains the
self-organization process by the way of transformation rules that are intrinsic
of a language system. This argument is verified in the paper with statistical
analyses of twelve randomly selected Chinese verb metaphors in a large-scale
diachronic corpus.
Related papers
- Discovering Conceptual Metaphors Across Topics and Media Types [40.43680452279358]
We present an unsupervised method that extracts linguistic metaphors from a corpus and uses a structured clustering approach to form groups corresponding to conceptual metaphors.<n>For example, left-leaning podcasts tend to conceptualize media stories as a weapon, while right-leaning sources commonly discuss the economy as a system subject to vertical changes.
arXiv Detail & Related papers (2026-08-06T23:43:08Z) - Learning the Cue or Learning the Word? Analyzing Generalization in Metaphor Detection for Verbs [54.86138668387175]
We analyze metaphor detection through RoBERTa, the shared backbone of many state-of-the-art systems, focusing on English verbs.<n>We introduce a controlled lexical hold-out setup where all instances of selected target lemmas are strictly excluded from fine-tuning, and compare predictions on these Held-out lemmas against Exposed lemmas.<n>While the model performs best on Exposed lemmas, it maintains robust performance on Held-out lemmas.
arXiv Detail & Related papers (2026-04-15T10:48:09Z) - Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions [53.690096725532726]
We analyze 297 English verb-object pairs (e.g., float idea vs. suggest idea) in 2M corpus sentences.<n>Using five NLP tools, we extract 2,293 cognitive and linguistic features capturing affective, lexical, syntactic, and discourse-level properties.<n>Cross-pair results show literal contexts have higher lexical frequency, cohesion, and structural regularity, while metaphorical contexts show greater affective load, imageability, lexical diversity, and constructional specificity.
arXiv Detail & Related papers (2026-04-09T14:08:57Z) - IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation [88.98544786373212]
Existing sentence representations primarily encode what a sentence says, rather than how it is expressed.<n>We develop sentence representations that capture style and dialect, decoupled from semantic content.<n>We introduce IDIOLEX, a framework for training models that combines supervision from a sentence's provenance with linguistic features of a sentence's content.
arXiv Detail & Related papers (2026-04-06T14:17:24Z) - Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation [6.0158981171030685]
We present a novel parallel dataset for the tasks of metaphor detection and interpretation that contains metaphor annotations in both Spanish and English.
We investigate language models' metaphor identification and understanding abilities through a series of monolingual and cross-lingual experiments.
arXiv Detail & Related papers (2024-04-10T14:44:48Z) - LMs stand their Ground: Investigating the Effect of Embodiment in
Figurative Language Interpretation by Language Models [0.0]
Figurative language is a challenge for language models since its interpretation deviates from their conventional order and meaning.
Yet, humans can easily understand and interpret metaphors as they can be derived from embodied metaphors.
This study shows how larger language models perform better at interpreting metaphoric sentences when the action of the metaphorical sentence is more embodied.
arXiv Detail & Related papers (2023-05-05T11:44:12Z) - Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions.
Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
arXiv Detail & Related papers (2022-10-14T02:35:19Z) - What Drives the Use of Metaphorical Language? Negative Insights from
Abstractness, Affect, Discourse Coherence and Contextualized Word
Representations [13.622570558506265]
Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives?
Many NLP approaches to metaphorical language rely on cognitive and (psycho-)linguistic insights and have successfully defined models of discourse coherence, abstractness and affect.
In this work, we build five simple models relying on established cognitive and linguistic properties to predict the use of a metaphorical vs. synonymous literal expression in context.
arXiv Detail & Related papers (2022-05-23T08:08:53Z) - Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
Figurative and metaphorical language are commonplace in discourse.
It remains an open question to what extent modern language models can interpret nonliteral phrases.
We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
arXiv Detail & Related papers (2022-04-26T23:42:22Z) - Modeling Target-Side Morphology in Neural Machine Translation: A
Comparison of Strategies [72.56158036639707]
Morphologically rich languages pose difficulties to machine translation.
A large amount of differently inflected word surface forms entails a larger vocabulary.
Some inflected forms of infrequent terms typically do not appear in the training corpus.
Linguistic agreement requires the system to correctly match the grammatical categories between inflected word forms in the output sentence.
arXiv Detail & Related papers (2022-03-25T10:13:20Z) - On the Impact of Temporal Representations on Metaphor Detection [1.6959319157216468]
State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using sequential metaphor classifiers based on neural networks.
This study examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings.
Results suggest that different word embeddings do impact on the metaphor detection task and some temporal word embeddings slightly outperform static methods on some performance measures.
arXiv Detail & Related papers (2021-11-05T08:43:21Z) - Metaphor Generation with Conceptual Mappings [58.61307123799594]
We aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs.
We propose to control the generation process by encoding conceptual mappings between cognitive domains.
We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems.
arXiv Detail & Related papers (2021-06-02T15:27:05Z) - MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding [22.756157298168127]
Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus.
For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data.
A task-based evaluation shows that human-written poems enhanced with metaphors are preferred 68% of the time compared to poems without metaphors.
arXiv Detail & Related papers (2021-03-11T16:39:19Z) - Lexical semantic change for Ancient Greek and Latin [61.69697586178796]
Associating a word's correct meaning in its historical context is a central challenge in diachronic research.
We build on a recent computational approach to semantic change based on a dynamic Bayesian mixture model.
We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models.
arXiv Detail & Related papers (2021-01-22T12:04:08Z) - Metaphoric Paraphrase Generation [58.592750281138265]
We use crowdsourcing to evaluate our results, as well as developing an automatic metric for evaluating metaphoric paraphrases.
We show that while the lexical replacement baseline is capable of producing accurate paraphrases, they often lack metaphoricity.
Our metaphor masking model excels in generating metaphoric sentences while performing nearly as well with regard to fluency and paraphrase quality.
arXiv Detail & Related papers (2020-02-28T16:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.