A Wordnet from the Ground Up

92 Chapter 3. Discovering Semantic Relatednessbut also the training phase can influence it. Worse still, the value of “good” k canchange with word x for the same MSR.Clustering techniques may help create better lists or groups of words. We wouldlike to find a method that identifies lists of tightly interlinked word groups representingnear-synonymy and close hypernymy, which could be added to plWordNet with as littleintervention of the linguists as possible.Standard partitioning clustering methods are ill-suited to the task of clusteringlemmas. They can assign one word to a single cluster, which is problematic forpolysemous lemmas. For lemmas that have one predominant meaning, only a clusterfor one sense will be created. For polysemous lemmas without a predominant meaningthe situation may be even less pleasant: such lemmas can lead to the creation of clustersthat mix lemmas that have more than one of the polysemous lemma senses. That iswhy we need specialized clustering method.Several clustering algorithms for the task of grouping words have been discussedin the literature. Among them, Clustering by Committee [CBC] (Pantel, 2003, Linand Pantel, 2002) has been reported to achieve especially good accuracy with respectto evaluation performed on the basis of PWN. It is often referred to in the literatureas one of the most interesting clustering algorithms (Pedersen, 2006).CBC relies only on a modestly advanced dependency parser and on a MSR based onPointwise Mutual Information [PMI] extended with a discounting factor (Lin and Pantel,2002). This MSR is a modification of Lin’s measure (Lin, 1998) analysed inSection 3.4 and in (Broda et al., 2008) in application to Polish. Both measures areclose to the RWF measure (Piasecki et al., 2007a) that achieves good accuracy insynonymy tests generated out of plWordNet (Section 3.3).Applications of CBC to languages other than English are rarely reported in theliterature. Tomuro et al. (2007) mentioned briefly some experiments with Japanese, butgave no results. Differences between languages, and especially differences in resourceavailability for different languages, can affect the construction of the similarity functionat the heart of CBC. CBC also crucially depends on several thresholds whose valueswere established experimentally. It is quite unclear to what extent they can be reusedor re-discovered for different languages and language resources.The CBC algorithm has been well described by its authors (Pantel, 2003, Linand Pantel, 2002). We will therefore only outline its general organisation, following(Lin and Pantel, 2002) and emphasising selected key points. We have reformulatedsome steps in order to name consistently all thresholds present in the algorithm. Otherwise,we keep the original names.I. Find most similar elements1. For each word e in the input set E, select k most similar words consid-