Mapping Political-Elite Networks in Europe with a Multilingual Joint Entity-Relation Extraction Pipeline

• URL: http://arxiv.org/abs/2606.27347v2
• Date: Mon, 29 Jun 2026 14:05:28 GMT
• Title: Mapping Political-Elite Networks in Europe with a Multilingual Joint Entity-Relation Extraction Pipeline
• Abstract summary: We present a modular, fully open-weight pipeline for multilingual joint entity-relation extraction.<n>It builds signed, temporal knowledge graphs from massive unstructured news corpora.<n>A full-coverage spot-check against a 3491-relation gold standard shows high textual correctness.
• Score: 0.03618585333595831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whether political elites organise into rent-seeking coalitions that capture public resources or civic networks that sustain governance is a central question in comparative politics. Yet observing these complex, informal, and adversarial ties at scale has historically required intensive manual coding, while automated text-as-data methods have largely been limited to simple co-occurrence. Recent large language model (LLM) approaches offer a path forward but often rely on proprietary APIs, lack cross-lingual capability, and struggle with scalable entity resolution. We present a modular, fully open-weight pipeline for multilingual joint entity-relation extraction that builds signed, temporal knowledge graphs from massive unstructured news corpora. It combines span-based named-entity recognition (NER) with a three-stage linking cascade mapping mentions to language-independent Wikidata identifiers; a high-throughput, ontology-constrained mixture-of-experts model then uses guided decoding to extract directed, signed relationships grounded in a domain ontology. A full-coverage spot-check against a 3491-relation gold standard shows high textual correctness (68.2% strict to 93.7% lenient). Two large-scale case studies validate the pipeline against the public record. In Austria, it reconstructs a political party's complete lifecycle, dating internal fractures and tracking personnel into successor factions and court convictions. In a Polish corpus, it uncovers the overlapping economic and governance networks of state-enterprise patronage, alongside the structurally balanced, signed conflict network of the polarized Civic Platform (Platforma Obywatelska, PO)--Law and Justice (Prawo i Sprawiedliwość, PiS) duopoly. By bridging raw multilingual text and structured relational data, our framework provides a robust, replicable foundation for cross-national empirical computational social science.

Related papers

• Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents [0.22940141855172028]
  We present a multi-agent framework that reconciles factual grounding with ideological alignment.<n>We operationalize the framework on the 2019 Flemish election, deploying the partisan agents in a hub-and-spoke negotiation arbitrated by a formateur.
  arXiv  Detail & Related papers  (2026-07-16T15:08:29Z)
• Elite Proxies, Algorithmic Bottlenecks, and Multi-Platform Information Cascades in the 2026 Iran War [0.0]
  This study tracks the transformation of President Trump's statements during the 2026 Iran War as they move from Truth Social into X-Twitter and Bluesky.<n>The results reveal pronounced behavioral divergence driven by socio-technical affordances.
  arXiv  Detail & Related papers  (2026-06-09T16:55:50Z)
• BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement [0.17398560678845076]
  BiST is a rigorously curated Bangla-English corpus for sentence-level grammatical classification.<n>The corpus is compiled from open-licensed encyclopedic sources and naturally composed conversational text.<n>BiST supports grammatical modeling tasks, including controlled text generation, automated feedback generation, and cross-lingual representation learning.
  arXiv  Detail & Related papers  (2026-04-06T14:22:46Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• PICASO: Permutation-Invariant Context Composition with State Space Models [98.91198288025117]
  State Space Models (SSMs) offer a promising solution by allowing a database of contexts to be mapped onto fixed-dimensional states.<n>We propose a simple mathematical relation derived from SSM dynamics to compose multiple states into one that efficiently approximates the effect of concatenating raw context tokens.<n>We evaluate our resulting method on WikiText and MSMARCO in both zero-shot and fine-tuned settings, and show that we can match the strongest performing baseline while enjoying on average 5.4x speedup.
  arXiv  Detail & Related papers  (2025-02-24T19:48:00Z)
• OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting [49.655711022673046]
  OneNet is an innovative framework that utilizes the few-shot learning capabilities of Large Language Models (LLMs) without the need for fine-tuning. OneNet is structured around three key components prompted by LLMs: (1) an entity reduction processor that simplifies inputs by summarizing and filtering out irrelevant entities, (2) a dual-perspective entity linker that combines contextual cues and prior knowledge for precise entity linking, and (3) an entity consensus judger that employs a unique consistency algorithm to alleviate the hallucination in the entity linking reasoning.
  arXiv  Detail & Related papers  (2024-10-10T02:45:23Z)
• Verified authors shape X/Twitter discursive communities [0.24999074238880484]
  This study focuses on the role of verified users as the main content creators in online political debates.<n>The analysis centers on three major Italian political events in 2022 - the Presidential election, a governmental crisis, and the general elections - occurring before the introduction of paid account verification.
  arXiv  Detail & Related papers  (2024-05-08T09:04:46Z)
• CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering [0.0]
  This paper introduces CommunityFish as an augmented version of Wordfish based on a hierarchical clustering, namely the Louvain algorithm, on the word space to yield communities as semantic and independent n-grams emerging from the corpus. This strategy emphasizes the interpretability of the results, since communities have a non-overlapping structure, hence a crucial informative power in discriminating parties or speakers, in addition to allowing a faster execution of the Poisson scaling model.
  arXiv  Detail & Related papers  (2023-08-28T19:52:18Z)
• mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning [54.523172171533645]
  Cross-lingual named entity recognition (CrossNER) faces challenges stemming from uneven performance due to the scarcity of multilingual corpora. We propose Multi-view Contrastive Learning for Cross-lingual Named Entity Recognition (mCL-NER) Our experiments on the XTREME benchmark, spanning 40 languages, demonstrate the superiority of mCL-NER over prior data-driven and model-based approaches.
  arXiv  Detail & Related papers  (2023-08-17T16:02:29Z)
• Optimizing text representations to capture (dis)similarity between political parties [1.2891210250935146]
  We look at the problem of modeling pairwise similarities between political parties. Our research question is what level of structural information is necessary to create robust text representation. We evaluate our models on the manifestos of German parties for the 2021 federal election.
  arXiv  Detail & Related papers  (2022-10-21T14:24:57Z)
• O-Dang! The Ontology of Dangerous Speech Messages [53.15616413153125]
  We present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. It provides a model for encoding both gold standard and single-annotator labels in the KG.
  arXiv  Detail & Related papers  (2022-07-13T11:50:05Z)
• Named Entity Recognition and Linking Augmented with Large-Scale Structured Data [3.211619859724085]
  We describe our submissions to the 2nd and 3rd SlavNER Shared Tasks held at BSNLP 2019 and BSNLP 2021. The tasks focused on the analysis of Named Entities in multilingual Web documents in Slavic languages with rich inflection. Our solution takes advantage of large collections of both unstructured and structured documents.
  arXiv  Detail & Related papers  (2021-04-27T20:10:18Z)
• Linguistic dependencies and statistical dependence [76.89273585568084]
  We use pretrained language models to estimate probabilities of words in context. We find that maximum-CPMI trees correspond to linguistic dependencies more often than trees extracted from non-contextual PMI estimate.
  arXiv  Detail & Related papers  (2021-04-18T02:43:37Z)

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.