A Wordnet from the Ground Up

3.4. Measures of Semantic Relatedness 73correspond to all context types taken into consideration. The initial value of featuresare the frequencies of occurrences of the given target LU in the corresponding lexicosyntacticcontexts. This raw information, however, is very noisy and not reliable.• Some features deliver little or no information. Consider, for example, very frequentadjectives with vague meaning, such as “nowy” (new, 627874 occurrencesin corpora) or “wielki” (large, great, 615785), “mój” (mine, 592976), or very frequentverbs that occur with many subjects, such as “być” (be, 6944204), “mieć”(have, 2332773), and so on. They result in large values of the correspondingfeatures (frequencies), occur with the majority of target LUs and make every LUrelated to every other LU.• Accidental feature values caused by very infrequent, mostly singular, occurrencesof the corresponding lexical elements with the target LUs have negligible influenceon the well-described frequent target LUs with many non-zero features, butcan relate some infrequent LUs to many others just because of a few accidentalfeature values, e.g. association of “pies” (dog) with “żelbeton” (reinforcedconcrete) found by noun-coordination constraint (NcC).• Raw feature values can also be biased by corpora in two ways: values of featuresfrom some subset can be increased (e.g., some specific modifiers repeatedly usedacross some set of documents) and for some subset of the target LUs the averagelevel of the values of their features can be increased in comparison to the rest ofthe target LUs (e.g., because LUs from the given subset occur more frequentlyin the corpora).Thus, most MSR extraction methods transform the initial raw frequencies before thefinal computation of the MSR value. Such a transformation is typically a combinationof filtering and weighting. The quality and behaviour of an MSR depend to a largeextent on the transformation applied. For example, in (Piasecki et al., 2007a), theincrease from 82.72% of accuracy in WBST+H to 86.09% was achieved only bychanging the transformation.Transformations proposed in the literature usually combine initial filtering basedon simple heuristics referring to frequencies with weighting based on the analysis ofstatistical association between the given target LU and features. The filtering functionscan be applied to both target LUs and features, in order to remove target LUs forwhich we do not have enough information, or to exclude from description features thatdo not deliver enough information. Mostly, a filtering function is defined as a simplecomparison with the threshold. In the case of target LUs, LU frequency and the numberof non-zero features are tested, e.g. Lin (1998) filtered out all target LUs occurringless than 100 times in the corpus of about 64 million words. For features, elimination