Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction
Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures. While such methods model nodes{'} local pairwise importance, they lack the capability to model global importance relative to other nodes of the graph. This causes such models to miss critical information in tasks where global ranking is a key component for the task, such as in keyphrase extraction. We address this shortcoming by allowing the proper incorporation of global information into the GCN family of models through the use of scaled node weights. In the context of keyphrase extraction, incorporating global random walk scores obtained from TextRank boosts performance significantly. With our proposed method, we achieve state-of-the-art results, bettering a strong baseline by an absolute 2{\%} increase in F1 score.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringKeyphrase ExtractionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
Although the application of Transformers in 3D point cloud processing has achieved significant progress and success, it is still challenging for existing 3D Transformer methods to efficiently and accurately learn both va…
ClassificationDecoderPoint Cloud ClassificationSegmentationGLocal-K: Global and Local Kernels for Recommender Systems
Recommender systems typically operate on high-dimensional sparse user-item matrices. Matrix completion is a very challenging task to predict one's interest based on millions of other users having each seen a small subset…
Collaborative FilteringMatrix CompletionRecommendation SystemsGlocal Information Bottleneck for Time Series Imputation
Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-world scenarios. Existing models typically o…
Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping
Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogene…
GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
Zero-shot anomaly detection (ZSAD) is crucial for detecting anomalous patterns in target datasets without using training samples, specifically in scenarios where there are distributional differences between the target do…
Anomaly DetectionContrastive LearningPrompt Learningzero-shot anomaly detection