A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition

• URL: http://arxiv.org/abs/2604.18944v1
• Date: Tue, 21 Apr 2026 00:46:48 GMT
• Title: A Mechanism and Optimization Study on the Impact of Information Density on User-Generated Content Named Entity Recognition
• Abstract summary: We show that surface-level noise symptoms share a unified root cause: low Information Density (ID)<n>We introduce Attention Spectrum Analysis (ASA) to quantify how reduced ID causally leads to attention blunting''<n>We propose the Window-Aware Optimization Module (WOM), an LLM-empowered, model-agnostic framework.
• Score: 1.005854289245731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Named Entity Recognition (NER) models trained on clean, high-resource corpora exhibit catastrophic performance collapse when deployed on noisy, sparse User-Generated Content (UGC), such as social media. Prior research has predominantly focused on point-wise symptom remediation -- employing customized fine-tuning to address issues like neologisms, alias drift, non-standard orthography, long-tail entities, and class imbalance. However, these improvements often fail to generalize because they overlook the structural sparsity inherent in UGC. This study reveals that surface-level noise symptoms share a unified root cause: low Information Density (ID). Through hierarchical confounding-controlled resampling experiments (specifically controlling for entity rarity and annotation consistency), this paper identifies ID as an independent key factor. We introduce Attention Spectrum Analysis (ASA) to quantify how reduced ID causally leads to ``attention blunting,'' ultimately degrading NER performance. Informed by these mechanistic insights, we propose the Window-Aware Optimization Module (WOM), an LLM-empowered, model-agnostic framework. WOM identifies information-sparse regions and utilizes selective back-translation to directionally enhance semantic density without altering model architecture. Deployed atop mainstream architectures on standard UGC datasets (WNUT2017, Twitter-NER, WNUT2016), WOM yields up to 4.5\% absolute F1 improvement, demonstrating robustness and achieving new state-of-the-art (SOTA) results on WNUT2017.

Related papers

• Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework [12.197772777447405]
  Image enhancement and restoration have become standard back-end operations on short-video and social media platforms to boost visual experience.<n>Existing IQA methods fail to capture the specific, localized anomalies caused by enhancement algorithms in real-world scenarios.<n>We formally define a new task-quality Anomaly Perception for image Enhancement (UEAP)<n>We contribute the first UEAP benchmark dataset, named UEAP-4k, curated from the real business scenarios.
  arXiv  Detail & Related papers  (2026-09-02T12:37:56Z)
• Large Vision-Language Models Get Lost in Attention [51.851592109135716]
  We propose a unified framework grounded in information theory and geometry to quantify the geometric and entropic nature of residual updates.<n>Applying this unified framework reveals a fundamental functional decoupling: Attention acts as a subspace-language operator focused on reconfiguration, whereas FFNs serve as subspace-language operators driving semantic innovation.
  arXiv  Detail & Related papers  (2026-05-07T04:45:52Z)
• EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels [85.78886153628663]
  Open-Set Domain Generalization aims to enable deep learning models to recognize unseen categories in new domains.<n>Label noise hinders open-set domain generalization by corrupting source-domain knowledge.<n>We propose Evidential Reliability-Aware Residual Flow Meta-Learning (EReLiFM) to bridge domain gaps.
  arXiv  Detail & Related papers  (2025-10-14T16:23:11Z)
• A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detection [0.0]
  Community detection in networks with overlapping structures remains a significant challenge.<n>We propose a semi-supervised graph autoencoder that combines graph multi-head attention and modularity to robustly detect overlapping communities.<n>Key innovations include a noise-resistant architecture and a semantic semi-supervised design optimized for community quality.
  arXiv  Detail & Related papers  (2025-05-09T11:34:07Z)
• Dissecting the Failure of Invariant Learning on Graphs [36.11431280689549]
  We develop a Structural Causal Model (SCM) to theoretically dissect the performance of two prominent invariant learning methods.<n>We propose Cross-environment Intra-class Alignment (CIA), which explicitly eliminates spurious features by aligning cross-environment representations conditioned on the same class.<n>We further propose CIA-LRA (Localized Reweighting Alignment) that leverages the distribution of neighboring labels to selectively align node representations.
  arXiv  Detail & Related papers  (2024-11-05T06:36:48Z)
• Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation [73.9145653659403]
  We show that Generative Error Correction models struggle to generalize beyond the specific types of errors encountered during training. We propose DARAG, a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. Our approach is simple, scalable, and both domain- and language-agnostic.
  arXiv  Detail & Related papers  (2024-10-17T04:00:29Z)
• Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection [35.71100602593928]
  We propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically, we formulate in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task. Experimental results on five scenes demonstrate the prominent generalization ability of our method, with an improvement of 3.9% mAP on the Night-Clear scene.
  arXiv  Detail & Related papers  (2024-05-24T05:34:23Z)
• UGMAE: A Unified Framework for Graph Masked Autoencoders [67.75493040186859]
  We propose UGMAE, a unified framework for graph masked autoencoders. We first develop an adaptive feature mask generator to account for the unique significance of nodes. We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information.
  arXiv  Detail & Related papers  (2024-02-12T19:39:26Z)
• SSPNet: Scale and Spatial Priors Guided Generalizable and Interpretable Pedestrian Attribute Recognition [23.55622798950833]
  A novel Scale and Spatial Priors Guided Network (SSPNet) is proposed for Pedestrian Attribute Recognition (PAR) models. SSPNet learns to provide reasonable scale prior information for different attribute groups, allowing the model to focus on different levels of feature maps. A novel IoU based attribute localization metric is proposed for Weakly-supervised Pedestrian Attribute localization (WPAL) based on the improved Grad-CAM for attribute response mask.
  arXiv  Detail & Related papers  (2023-12-11T00:41:40Z)
• Discovering Dynamic Causal Space for DAG Structure Learning [64.763763417533]
  We propose a dynamic causal space for DAG structure learning, coined CASPER. It integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG.
  arXiv  Detail & Related papers  (2023-06-05T12:20:40Z)
• Calibrated Feature Decomposition for Generalizable Person Re-Identification [82.64133819313186]
  Calibrated Feature Decomposition (CFD) module focuses on improving the generalization capacity for person re-identification. A calibrated-and-standardized Batch normalization (CSBN) is designed to learn calibrated person representation.
  arXiv  Detail & Related papers  (2021-11-27T17:12:43Z)

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.