Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis

• URL: http://arxiv.org/abs/2602.12881v1
• Date: Fri, 13 Feb 2026 12:31:30 GMT
• Title: Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis
• Authors: Oktay Karakuş,
• Abstract summary: We present a graph-based framework for unsupervised discovery and evaluation of semantic communities in K-pop lyrics.<n>By constructing a similarity graph over lyric texts, we uncover stable micro-theme communities without genre, artist, or language supervision.<n>Across multiple settings, boundary-spanning lyrics exhibit higher lexical diversity and lower repetition compared to core community members.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale lyric corpora present unique challenges for data-driven analysis, including the absence of reliable annotations, multilingual content, and high levels of stylistic repetition. Most existing approaches rely on supervised classification, genre labels, or coarse document-level representations, limiting their ability to uncover latent semantic structure. We present a graph-based framework for unsupervised discovery and evaluation of semantic communities in K-pop lyrics using line-level semantic representations. By constructing a similarity graph over lyric texts and applying community detection, we uncover stable micro-theme communities without genre, artist, or language supervision. We further identify boundary-spanning songs via graph-theoretic bridge metrics and analyse their structural properties. Across multiple robustness settings, boundary-spanning lyrics exhibit higher lexical diversity and lower repetition compared to core community members, challenging the assumption that hook intensity or repetition drives cross-theme connectivity. Our framework is language-agnostic and applicable to unlabeled cultural text corpora.

Related papers

• Linguistically Informed Graph Model and Semantic Contrastive Learning for Korean Short Text Classification [2.4071330817126477]
  We propose LIGRAM, a hierarchical heterogeneous graph model for Korean short-text classification.<n>The proposed model constructs sub-graphs at the morpheme, part-of-speech, and named-entity levels and hierarchically integrates them to compensate for the limited contextual information in short texts.<n>We evaluate LIGRAM on four Korean short-text datasets, where it consistently outperforms existing baseline models.
  arXiv  Detail & Related papers  (2026-03-04T02:17:13Z)
• Multigranular Evaluation for Brain Visual Decoding [5.19485079754946]
  Existing evaluation protocols for brain visual decoding rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions.<n>We introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground truth images.<n>For the structural level, we introduce a hierarchical suite of segmentation-based metrics, including foreground, semantic, instance, and component masks, anchored in granularity-aware correspondence across mask structures.<n>For the semantic level, we extract structured scene representations encompassing objects, attributes, and relationships using multimodal large
  arXiv  Detail & Related papers  (2025-07-10T17:59:24Z)
• Synthetic Lyrics Detection Across Languages and Genres [4.987546582439803]
  Large language models (LLMs) to generate music content, particularly lyrics, has gained in popularity.<n>Previous research has explored content detection in various domains, but no work has focused on the text modality, lyrics, in music.<n>We curated a diverse dataset of real and synthetic lyrics from multiple languages, music genres, and artists.<n>We performed a thorough evaluation of existing synthetic text detection approaches on lyrics, a previously unexplored data type.<n>Following both music and industrial constraints, we examined how well these approaches generalize across languages, scale with data availability, handle multilingual language content, and perform on novel genres in few-shot settings
  arXiv  Detail & Related papers  (2024-06-21T15:19:21Z)
• Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
  GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
  arXiv  Detail & Related papers  (2024-06-18T13:35:25Z)
• Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models [5.736540322759929]
  We make the first attempt to model a full music piece under the realization of compositional hierarchy. High-level languages reveal whole-song form, phrase, and cadence, whereas the low-level languages focus on notes, chords, and their local patterns. Experiments and analysis show that our model is capable of generating full-piece music with recognizable global verse-chorus structure and cadences.
  arXiv  Detail & Related papers  (2024-05-16T08:48:23Z)
• Unsupervised Melody-to-Lyric Generation [91.29447272400826]
  We propose a method for generating high-quality lyrics without training on any aligned melody-lyric data. We leverage the segmentation and rhythm alignment between melody and lyrics to compile the given melody into decoding constraints. Our model can generate high-quality lyrics that are more on-topic, singable, intelligible, and coherent than strong baselines.
  arXiv  Detail & Related papers  (2023-05-30T17:20:25Z)
• Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding [143.5927158318524]
  Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. We introduce a new Compositional Temporal Grounding task and construct two new dataset splits. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization.
  arXiv  Detail & Related papers  (2023-01-22T08:02:23Z)
• Contextual information integration for stance detection via cross-attention [59.662413798388485]
  Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information. We propose an approach to integrate contextual information as text.
  arXiv  Detail & Related papers  (2022-11-03T15:04:29Z)
• Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers [86.64972552583941]
  We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
  arXiv  Detail & Related papers  (2022-05-23T16:47:37Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics [21.944835086749375]
  Learning and analyzing rap lyrics is a significant basis for many web applications. We propose a hierarchical attention variational autoencoder framework (HAVAE) A feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation.
  arXiv  Detail & Related papers  (2021-03-23T15:13:21Z)
• Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features [0.0]
  We provide large poetry corpora for English and German, and annotate prosodic features in smaller corpora to train corpus driven neural models. We show that BiLSTM-CRF models with syllable embeddings outperform a CRF baseline and different BERT-based approaches.
  arXiv  Detail & Related papers  (2021-02-17T16:38:57Z)

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.