Publication

Analysis of a Synonymy Network

Jean-Cédric Chappelier
2004
Reports

Abstract

The purpose of this report is to present the study of a linguistic network based on the relation of synonymy. The network has been extracted from a dictionary of synonyms in French. Due to errors and ambiguities in the data, the synonyms are not always grouped into distinct components. To refine the results, we apply a clustering algorithm to the network. Each densely connected cluster is considered to correspond to a general concept in the language. Finally we develop a new general method, based on the introduction of noise in the network, to identify nodes which lie on the border between clusters and to evaluate the robustness of the clustering. This method can be applied with any clustering algorithm, provided that the algorithm works on weighted networks.

Official source

https://infoscience.epfl.ch/entities/publication/10514afe-2686-4ac1-9acc-4dc6ee5a7e1c

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Jean-Cédric Chappelier
2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/10514afe-2686-4ac1-9acc-4dc6ee5a7e1c

About this result

Related concepts (32)

Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.

Hierarchical clustering

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

K-means clustering

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.

Related publications (32)

Analysis of a Synonymy Network

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