A Wordnet from the Ground Up

3.5. Sense Discovery by Clustering 89documents can be used; for details see (Guha et al., 2000) and (Broda and Piasecki,2008a). Even if documents are not very similar, they can form a cluster in ROCK ifthey have many common neighbours. This clusters of unusual shapes possible.The other clustering algorithm we considered is called Growing Hierarchical Self-Organising Map [GHSOM] (Rauber et al., 2002) an extension of Self-Organising Map[SOM] (Kohonen et al., 2000). SOM is an artificial neural network. Every neuronconsists of a weight vector and a vector of positions in the map 20 . Training SOM is donein an unsupervised manner by applying a winner-takes-all strategy. Every document isdelivered to the network several times. The neuron most similar to a given documentis the winner. Weights of the winning neuron and neurons in its neighbourhood 21are updated to be even more similar to the input pattern. The learning algorithm isconstructed so that the neighbourhood of a neuron and the rate of weight updatingdecrease over time.The GHSOM algorithm addresses one of SOM’s most important drawbacks – thea priori definition of the map structure. Rauber et al. (2002) proposed an algorithmfor growing SOM both in terms of the number of map neurons and the hierarchy.Clustering results will be used in the extraction of polysemy information, labellingclusters with keywords and generation of a basic structure for a wordnet, so we wantedto be sure to select clustering algorithms that performs well on collections of Polishdocuments.There exists a few approaches to the evaluation of clustering (Forster, 2006). Forexample, one can study the theoretical properties of an algorithm, or measure somemathematical properties of the resulting clusters. In some domains those methodscan be appropriate, but we argue that for the domain of documents the most suitableevaluation method is by referring to external criteria, such as a comparison of theresults with manually created pre-existing categories.Our evaluation used parts of the Polish daily paper “Dziennik Polski”, included inthe IPI PAN Corpus [IPIC] (Przepiórkowski, 2004). It has been manually partitionedinto categories: Economy, Sport, Magazine, Home News, and so on. Both ROCK andGHSOM gave results satisfactory in comparison to the “Dziennik Polski” data (Brodaand Piasecki, 2008a). A manual inspection of the produced clusters confirmed thoseresults. We did not find any mixing of major topics in groups, for example there wasno document from Sport put into clusters with documents talking about Economy.The algorithms also found more categories than are actually present in the corpus. Forexample, different sport disciplines were partitioned into separate groups. An important20 For us, a map is a two-dimensional grid, with neurons placed in the nodes of the grid. This isnot the only possible representation for SOM: a map can be hexagon-based or neurons can be placed ina three dimensional space.21 Note that this neighbourhood is different from neighbourhood in ROCK. In SOM it is defined simplyas certain number of neurons in the map that are close to the winning neuron.