A Computational Approach to Style in American Poetry

• URL: http://arxiv.org/abs/2310.09357v1
• Date: Fri, 13 Oct 2023 18:49:14 GMT
• Title: A Computational Approach to Style in American Poetry
• Authors: David M. Kaplan, David M. Blei
• Abstract summary: We develop a method to assess the style of American poems and to visualize a collection of poems in relation to one another. qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.
• Score: 19.41186389974801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another. Qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. These features are used to discover comprehensive stylistic information from a poem's multi-layered latent structure, and to compute distances between poems in this space. Visualizations provide ready access to the analytical components. We demonstrate our method on several collections of poetry, showing that it better delineates poetry style than the traditional word-occurrence features that are used in typical text analysis algorithms. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.

Related papers

• Understanding Literary Texts by LLMs: A Case Study of Ancient Chinese Poetry [9.970908656435066]
  In genres such as poetry, jokes, and short stories, numerous AI tools have emerged, offering refreshing new perspectives. evaluating literary works is often complex and hard to fully quantify, which directly hinders the further development of AI creation. This paper attempts to explore the mysteries of literary texts from the perspective of large language models.
  arXiv  Detail & Related papers  (2024-08-22T04:25:06Z)
• Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets [3.0040661953201475]
  Large language models (LLMs) can now generate and recognize poetry. We develop a task to evaluate how well LLMs recognize one aspect of English-language poetry. We show that state-of-the-art LLMs can successfully identify both common and uncommon fixed poetic forms.
  arXiv  Detail & Related papers  (2024-06-27T05:36:53Z)
• ALADIN-NST: Self-supervised disentangled representation learning of artistic style through Neural Style Transfer [60.6863849241972]
  We learn a representation of visual artistic style more strongly disentangled from the semantic content depicted in an image. We show that strongly addressing the disentanglement of style and content leads to large gains in style-specific metrics.
  arXiv  Detail & Related papers  (2023-04-12T10:33:18Z)
• A Method to Judge the Style of Classical Poetry Based on Pre-trained Model [13.899056358137287]
  This paper builds the most perfect data set of Chinese classical poetry at present, trains a BART-poem pre-trained model on this data set, and puts forward a generally applicable poetry style judgment method. Experiments show that the judgment results of the tested poetry work are basically consistent with the conclusions given by critics of previous dynasties, verify some avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry style recognition in the Tang and Song dynasties.
  arXiv  Detail & Related papers  (2022-11-09T03:11:15Z)
• PoeticTTS -- Controllable Poetry Reading for Literary Studies [21.29478270833139]
  We resynthesise poems by cloning prosodic values from a human reference recitation, and afterwards make use of fine-grained prosody control to manipulate the synthetic speech. We find that finetuning our TTS model on poetry captures poetic intonation patterns to a large extent which is beneficial for prosody cloning and manipulation.
  arXiv  Detail & Related papers  (2022-07-11T13:15:27Z)
• Digital Editions as Distant Supervision for Layout Analysis of Printed Books [76.29918490722902]
  We describe methods for exploiting this semantic markup as distant supervision for training and evaluating layout analysis models. In experiments with several model architectures on the half-million pages of the Deutsches Textarchiv (DTA), we find a high correlation of these region-level evaluation methods with pixel-level and word-level metrics. We discuss the possibilities for improving accuracy with self-training and the ability of models trained on the DTA to generalize to other historical printed books.
  arXiv  Detail & Related papers  (2021-12-23T16:51:53Z)
• Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution [74.27826764855911]
  We employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. Our experiments, carried out on three different datasets, using two different machine learning methods, show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.
  arXiv  Detail & Related papers  (2021-10-27T06:25:31Z)
• CCPM: A Chinese Classical Poetry Matching Dataset [50.90794811956129]
  We propose a novel task to assess a model's semantic understanding of poetry by poem matching. This task requires the model to select one line of Chinese classical poetry among four candidates according to the modern Chinese translation of a line of poetry. To construct this dataset, we first obtain a set of parallel data of Chinese classical poetry and modern Chinese translation.
  arXiv  Detail & Related papers  (2021-06-03T16:49:03Z)
• A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking [98.1953404873897]
  We conduct a brief yet comprehensive review of existing literature and outline three meta-architectures. Based on this, we summarize specific strategies for various applications of deep hiding, including steganography, light field messaging and watermarking.
  arXiv  Detail & Related papers  (2021-03-02T10:01:03Z)
• MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space [79.70053419040902]
  We propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-13T03:31:29Z)

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.