CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies

• URL: http://arxiv.org/abs/2311.11301v1
• Date: Sun, 19 Nov 2023 11:22:00 GMT
• Title: CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
• Abstract summary: We release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. CHAMP significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels.
• Score: 42.58721170430892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements.

Related papers

• Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents [81.79110139097297]
  General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions.<n>We reframe instruction-following clustering as a generative task and train large reasoning models as autonomous clustering agents.<n>Our reasoning-driven training pipeline enables LRMs to interpret high-level clustering instructions and then infer the corresponding latent groupings.
  arXiv  Detail & Related papers  (2026-03-06T10:15:54Z)
• Multi-Vector Index Compression in Any Modality [73.7330345057813]
  Late interaction has emerged as a dominant paradigm for information retrieval in text, images, visual documents, and videos.<n>We introduce four approaches for index compression: sequence resizing, memory tokens, hierarchical pooling, and a novel attention-guided clustering (AGC)<n>AGC uses an attention-guided mechanism to identify the most semantically salient regions of a document as cluster centroids and to weight token aggregation.
  arXiv  Detail & Related papers  (2026-02-24T18:57:33Z)
• MRGSEM-Sum: An Unsupervised Multi-document Summarization Framework based on Multi-Relational Graphs and Structural Entropy Minimization [15.596156608713347]
  MRGSEM-Sum is an unsupervised multi-document summarization framework based on multi-relational graphs and structural entropy minimization.<n>We introduce a position-aware compression mechanism to distill each cluster, generating concise and informative summaries.
  arXiv  Detail & Related papers  (2025-07-31T10:14:03Z)
• Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs [51.13363550716544]
  Deep graph clustering is an unsupervised task aimed at partitioning nodes with incomplete attributes into distinct clusters.<n>Existing imputation methods for attribute-missing graphs often fail to account for the varying amounts of information available across node neighborhoods.<n>We propose Divide-Then-Rule Graph Completion (DTRGC) to address this issue.
  arXiv  Detail & Related papers  (2025-07-12T03:33:19Z)
• HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization [0.0]
  HERCULES is an algorithm and Python package designed for hierarchical k-means clustering of diverse data types.<n>It generates semantically rich titles and descriptions for clusters at each level of the hierarchy.<n>An interactive visualization tool facilitates thorough analysis and understanding of the clustering results.
  arXiv  Detail & Related papers  (2025-06-24T20:22:00Z)
• Hierarchical clustering with maximum density paths and mixture models [39.42511559155036]
  Hierarchical clustering is an effective and interpretable technique for analyzing structure in data. It is particularly helpful in settings where the exact number of clusters is unknown, and provides a robust framework for exploring complex datasets. Our method addresses this limitation by leveraging a two-stage approach, first employing a Gaussian or Student's t mixture model to overcluster the data, and then hierarchically merging clusters based on the induced density landscape. This approach yields state-of-the-art clustering performance while also providing a meaningful hierarchy, making it a valuable tool for exploratory data analysis.
  arXiv  Detail & Related papers  (2025-03-19T15:37:51Z)
• I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies [11.69714244591334]
  We show that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective over it. We conclude by verifying the utility of our proposed techniques across datasets, hierarchies, and partitioning schemes.
  arXiv  Detail & Related papers  (2025-02-19T18:03:52Z)
• OMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation [69.37484603556307]
  Un Semantic segmenting (USS) involves segmenting images without relying on predefined labels. We introduce a novel approach called Optimally Matched Hierarchy (OMH) to simultaneously address the above issues. Our OMH yields better unsupervised segmentation performance compared to existing USS methods.
  arXiv  Detail & Related papers  (2024-03-11T09:46:41Z)
• Generating Hierarchical Structures for Improved Time Series Classification Using Stochastic Splitting Functions [0.0]
  This study introduces a novel hierarchical divisive clustering approach with splitting functions (SSFs) to enhance classification performance in multi-class datasets through hierarchical classification (HC) The method has the unique capability of generating hierarchy without requiring explicit information, making it suitable for datasets lacking prior knowledge of hierarchy.
  arXiv  Detail & Related papers  (2023-09-21T10:34:50Z)
• Efficient Multi-View Graph Clustering with Local and Global Structure Preservation [59.49018175496533]
  We propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG) Specifically, EMVGC-LG jointly optimize anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number.
  arXiv  Detail & Related papers  (2023-08-31T12:12:30Z)
• Hierarchical clustering with dot products recovers hidden tree structure [53.68551192799585]
  In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic graphical model.
  arXiv  Detail & Related papers  (2023-05-24T11:05:12Z)
• Contrastive Multi-view Hyperbolic Hierarchical Clustering [33.050054725595736]
  We propose Contrastive Multi-view Hyperbolic Hierarchical Clustering (CMHHC) It consists of three components, i.e., multi-view alignment learning, aligned feature similarity learning, and continuous hyperbolic hierarchical clustering. Experimental results on five real-world datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-05-05T12:56:55Z)
• Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework [75.79736930414715]
  We present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint.
  arXiv  Detail & Related papers  (2022-04-27T21:41:44Z)
• Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees [5.877624540482919]
  We propose a novel hierarchical clustering algorithm, in which, while building the clustering dendrogram, we can effectively detect the representative point. Under our analysis, the proposed algorithm has O(nlogn) time-complexity and O(logn) space-complexity, indicating that it has the scalability in handling massive data.
  arXiv  Detail & Related papers  (2021-11-11T07:36:55Z)
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks [6.900303913555705]
  Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. We propose ours for joint learning of cluster embeddings as well as cluster-aware HIN embedding.
  arXiv  Detail & Related papers  (2021-08-09T11:36:36Z)
• Interactive Steering of Hierarchical Clustering [30.371250297444703]
  We present an interactive steering method to visually supervise constrained hierarchical clustering by utilizing both public knowledge (e.g., Wikipedia) and private knowledge from users. The novelty of our approach includes 1) automatically constructing constraints for hierarchical clustering using knowledge (knowledge-driven) and intrinsic data distribution (data-driven) To clearly convey the hierarchical clustering results, an uncertainty-aware tree visualization has been developed to enable users to quickly locate the most uncertain sub-hierarchies.
  arXiv  Detail & Related papers  (2020-09-21T05:26:07Z)
• Scalable Hierarchical Clustering with Tree Grafting [66.68869706310208]
  Grinch is a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions. Grinch is motivated by a new notion of separability for clustering with linkage functions.
  arXiv  Detail & Related papers  (2019-12-31T20:56:15Z)

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.