Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety bias

• URL: http://arxiv.org/abs/2110.06847v2
• Date: Wed, 29 Mar 2023 11:35:52 GMT
• Title: Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety bias
• Abstract summary: We show that the essence of meaning conveyed by words is instead best described by a compass-like power-danger framework. We introduce and explore synousionyms' and antousionyms' -- ousiometric counterparts of synonyms and antonyms. We use our findings to construct and test a prototype ousiometer'
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We define `ousiometrics' to be the study of essential meaning in whatever context that meaningful signals are communicated, and `telegnomics' as the study of remotely sensed knowledge. From work emerging through the middle of the 20th century, the essence of meaning has become generally accepted as being well captured by the three orthogonal dimensions of evaluation, potency, and activation (EPA). By re-examining first types and then tokens for the English language, and through the use of automatically annotated histograms -- `ousiograms' -- we find here that: 1. The essence of meaning conveyed by words is instead best described by a compass-like power-danger (PD) framework, and 2. Analysis of a disparate collection of large-scale English language corpora -- literature, news, Wikipedia, talk radio, and social media -- shows that natural language exhibits a systematic bias toward safe, low danger words -- a reinterpretation of the Pollyanna principle's positivity bias for written expression. To help justify our choice of dimension names and to help address the problems with representing observed ousiometric dimensions by bipolar adjective pairs, we introduce and explore `synousionyms' and `antousionyms' -- ousiometric counterparts of synonyms and antonyms. We further show that the PD framework revises the circumplex model of affect as a more general model of state of mind. Finally, we use our findings to construct and test a prototype `ousiometer', a telegnomic instrument that measures ousiometric time series for temporal corpora. We contend that our power-danger ousiometric framework provides a complement for entropy-based measurements, and may be of value for the study of a wide variety of communication across biological and artificial life.

Related papers

• Lexical Consensus: Grounded Word Learning and Shared Meaning in Artificial Agents [0.0]
  This paper introduces Lexical Consensus, an experimental framework for studying grounded word learning over a structured perceptual substrate.<n>We test whether agents can acquire artificial labels for visual concepts, generalize them bidirectionally, and stabilize them across controlled settings.<n>The main result is a robust perceptual-coherence gradient: native categories are easiest to learn, coherent overextensions remain learnable, mid-range disjunctive concepts degrade, and far-disjunctive concepts approach chance.
  arXiv  Detail & Related papers  (2026-06-20T20:08:07Z)
• 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)
• The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instruments [0.0]
  The paper argues that scientific meaning analysis motivates a distinct family of objectives - scientific usability.<n>The paper assesses static word embeddings and contextual transformer representations against these requirements.<n>As the field debates the limits of scale-first progress, measurement-ready representations offer a principled new frontier.
  arXiv  Detail & Related papers  (2026-03-10T18:08:53Z)
• On measuring grounding and generalizing grounding problems [0.0]
  The symbol grounding problem asks how tokens cat can be about cats, as opposed to mere shapes manipulated in calculus.<n>We recast grounding from a binary judgment into an audit across desiderata, each indexed by an evaluation.<n>We apply this framework to four grounding modes (symbolic; referential; vectorial; relational) and three case studies.
  arXiv  Detail & Related papers  (2025-12-05T22:58:47Z)
• Novelty and Impact of Economics Papers [0.25817216954554184]
  Two dimensions: textitspatial novelty and textittemporal novelty<n>We develop metrics that quantify a paper's location relative to the full-text literature.<n>These two dimensions predict systematically different outcomes.
  arXiv  Detail & Related papers  (2025-11-03T04:12:03Z)
• Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning [84.94709351266557]
  We focus on the trustworthiness of language models with respect to retrieval augmentation. We deem that retrieval-augmented language models have the inherent capabilities of supplying response according to both contextual and parametric knowledge. Inspired by aligning language models with human preference, we take the first step towards aligning retrieval-augmented language models to a status where it responds relying merely on the external evidence.
  arXiv  Detail & Related papers  (2024-10-22T09:25:21Z)
• The Shape of Word Embeddings: Quantifying Non-Isometry With Topological Data Analysis [10.242373477945376]
  We use persistent homology from topological data analysis to measure the distances between language pairs from the shape of their unlabeled embeddings. To distinguish whether these differences are random training errors or capture real information about the languages, we use the computed distance matrices to construct language phylogenetic trees over 81 Indo-European languages.
  arXiv  Detail & Related papers  (2024-03-30T23:51:25Z)
• Identifying and interpreting non-aligned human conceptual representations using language modeling [0.0]
  We show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains. We find that blind individuals more strongly associate social and cognitive meanings to verbs related to motion. For some verbs, representations of blind and sighted are highly similar.
  arXiv  Detail & Related papers  (2024-03-10T13:02:27Z)
• Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization [51.89486520806639]
  We propose a novel Synonym Generalization (SynGen) framework that recognizes the biomedical concepts contained in the input text using span-based predictions. We extensively evaluate our approach on a wide range of benchmarks and the results verify that SynGen outperforms previous dictionary-based models by notable margins.
  arXiv  Detail & Related papers  (2023-05-22T14:36:32Z)
• Visual Referential Games Further the Emergence of Disentangled Representations [0.12891210250935145]
  This paper investigates how do compositionality at the level of emerging languages, disentanglement at the level of the learned representations, and systematicity relate to each other in the context of visual referential games.
  arXiv  Detail & Related papers  (2023-04-27T20:00:51Z)
• An Informational Space Based Semantic Analysis for Scientific Texts [62.997667081978825]
  This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts. The representation of scientific-specific meaning is standardised by replacing the situation representations, rather than psychological properties. The research in this paper conducts the base for the geometric representation of the meaning of texts.
  arXiv  Detail & Related papers  (2022-05-31T11:19:32Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• A cognitively driven weighted-entropy model for embedding semantic categories in hyperbolic geometry [0.0]
  An unsupervised and cognitively driven weighted-entropy method for embedding semantic categories in hyperbolic geometry is proposed. The model is driven by two fields of research in cognitive linguistics: the statistical learning theory of language acquisition and the proposal of using high-dimensional networks to represent semantic knowledge in cognition. Results show that this new approach can successfully model and map the semantic relationships of popularity and similarity for most of the basic color and kinship words in English.
  arXiv  Detail & Related papers  (2021-12-13T18:33:45Z)
• Clinical Named Entity Recognition using Contextualized Token Representations [49.036805795072645]
  This paper introduces the technique of contextualized word embedding to better capture the semantic meaning of each word based on its context. We pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) Explicit experiments show that our models gain dramatic improvements compared to both static word embeddings and domain-generic language models.
  arXiv  Detail & Related papers  (2021-06-23T18:12:58Z)
• Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
  We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels. We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-20T17:56:24Z)
• Inferring symmetry in natural language [6.962103403356227]
  We present a methodological framework for inferring symmetry of verb predicates in natural language. Our work integrates existing approaches to symmetry in natural language and suggests how symmetry inference can improve systematicity in state-of-the-art language models.
  arXiv  Detail & Related papers  (2020-10-16T01:25:01Z)
• Analysing Lexical Semantic Change with Contextualised Word Representations [7.071298726856781]
  We propose a novel method that exploits the BERT neural language model to obtain representations of word usages. We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.
  arXiv  Detail & Related papers  (2020-04-29T12:18:14Z)

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.