Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems

@inproceedings{Biemann2006ChineseW,
  title={Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems},
  author={Chris Biemann},
  year={2006}
}
We introduce Chinese Whispers, a randomized graph-clustering algorithm, which is time-linear in the number of edges. After a detailed definition of the algorithm and a discussion of its strengths and weaknesses, the performance of Chinese Whispers is measured on Natural Language Processing (NLP) problems as diverse as language separation, acquisition of syntactic word classes and word sense disambiguation. At this, the fact is employed that the small-world property holds for many graphs in NLP. 

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References

SHOWING 1-10 OF 20 REFERENCES
Hierarchical Clustering of Words and Application to NLP Tasks
TLDR
A data-driven method for hierarchical clustering of words and clusters of multiword compounds, which can avoid the data sparseness problem which is ubiquitous in corpus statistics.
Word Sense Induction: Triplet-Based Clustering and Automatic Evaluation
TLDR
This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs, which enables automatic parameter optimization of the WSI algorithm.
Language-Independent Methods for Compiling Monolingual Lexical Data
TLDR
A flexible, portable and language-independent infrastructure for setting up large monolingual language corpora and the extraction and usage of sentence-based word collocations is discussed in detail.
Disentangling from Babylonian Confusion - Unsupervised Language Identification
TLDR
Evaluation on 7-lingual corpora and bilingual corpora show that the quality of classification is comparable to supervised approaches and works almost error-free from 100 sentences per language on.
On the Nature of Structure and Its Identification
TLDR
A new and lucid structure measure, the so-called weighted partial connectivity, Λ, whose maximization defines a graph's structure is introduced, which results in a new splitting theorem concerning the well-known minimum cut splitting measure.
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation
TLDR
A novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is demonstrated, resulting in an optimal solution equivalent to that obtained by partitioning the complete equivalent tree and is able to handle very large graphs with several hundred thousand vertices.
On the NP-Completeness of Some Graph Cluster Measures
TLDR
It is proved that the decision problems associated with the optimization tasks of finding clusters that are optimal with respect to these fitness measures are NP-complete.
A cluster algorithm for graphs
TLDR
The MCL~algorithm and process, convergence towards equilibrium states, interpretation of the states as clusterings, and implementation and scalability are described.
Small worlds: the dynamics of networks between order and randomness
  • Jie Wu
  • Computer Science
    SGMD
  • 2002
Everyone knows the small-world phenomenon: soon after meeting a stranger, we are surprised to discover that we have a mutual friend, or we are connected through a short chain of acquaintances. In his
WHICH TIES TO CHOOSE? A SURVEY OF SOCIAL NETWORKS MODELS FOR AGENT-BASED SOCIAL SIMULATIONS
TLDR
This paper proposes a state of the art about models of networks developed in several fields, in order to help modellers to choose relevant models con-cerning their problematic, to test the so-called “social net-work” effect.
...
...