A Wordnet from the Ground Up

74 Chapter 3. Discovering Semantic Relatednesscriteria can be based on the number of LUs described (the number of non-zero cellsfor the given feature), total feature frequency (with all LUs: a sum over the column)or statistical analysis, as in (Geffet and Dagan, 2004) discussed in a while.Hindle (1990) applied Mutual Information (MI) to compute feature weights inrelation to particular target LUs. Weights express the strength of association betweena target LU and a feature. Lin (1998) used a slightly modified version of MI based onthe ratio of the information shared and total description. Lin and Pantel (2002) appliedPointwise Mutual Information modified by a discounting factor during LU semanticsimilarity computation – see the generalised version in (Mohammad and Hirst, 2006).Geffet and Dagan (2004) introduced Relative Feature Focus (RFF), a feature-weightingfunction based on a two-step transformation. First they extract Lin’s MSR, filtering outfeatures with the overall frequency below 10 and MI weight below 4. Next, they recomputethe value of a feature f in LU u as the sum of MSR values of LUs most relatedto u such that they have a non-zero value for f. The final MSR(RFF) is calculated fromthe new feature values. Weeds and Weir (2005) proposed “a flexible, parameterisedframework for calculating” MSR, based on the idea of casting the problem as a CooccurrenceRetrieval Model (CRM). CRM describes semantic relatedness of two LUsin terms of weighted precision and recall of feature sharing between them. Of severalweighting functions applied, the best results came with MI and t-score measures.The method of transformation is often tightly coupled with the computation ofthe final MSR value, e.g. (Lin, 1998, Weeds and Weir, 2005), but vector similaritymeasures independent of the weight function are also applied, e.g. cosine measuresor Jaccard coefficient (Mohammad and Hirst, 2006). All these transformations stillassociate the calculated value of a feature with the initial frequency. For example, inthe case of Lin’s measure more frequent features do not only get values higher than lessfrequent features; the value level of frequent features is also higher than the value levelof those less frequent. We have noticed that this phenomenon negatively affects theaccuracy of MSR (Piasecki et al., 2007a). In a new weighting function proposed, thez-score measure was combined with a Rank Weight Function [RWF], a transformationfrom values to ranks.The main idea behind RWF is to put more emphasis in the description of LUmeaning on the identification of features most relevant to this LU. The calculation ofthe exact values of the strength of their association with the target LU is less important.We believe that these values are largely the artefact of the biased corpus frequencies,so one should not depend on them too strictly during row vector similarity calculation.The particular order of relevance of the features delivers clearer information. In RWF,the meaning of the given LU is described by an ordered set of relevant features, andthe meaning of two LUs can be compared on the corresponding sequences of featuresordered by relevance. In order to keep the correlation between relevance and feature