A Wordnet from the Ground Up

4.3. Generic Patterns Verified Statistically 1134. Instance extraction: the selected patterns are used to extract instances; the instancesare next statistically evaluated using the patterns that match their occurrences;the m top instances are selected and kept for the next iteration –a possible expansion phase for extended retrieval of instances can take placebefore the selection.The first step is performed once at the beginning. Lacking a stop condition (itdepends on the number of extracted patterns and average pattern score decrease inrelation to the previous iteration), the next iteration starts from step 2.Preprocessing used the Alembic Workbench part-of-speech tagger (Day et al., 1997)but no shallow parser. Multiword terms (if left unrecognised) would decrease theaccuracy of the extraction algorithm, because of instances generated from parts of thecomplex terms. We noticed this problem in the case of manually constructed patternsdiscussed in Section 4.1. Instead of using a shallow parser, Espresso applied a definitionof multiword terms as a regular expression (Pantel and Pennacchiotti, 2006, p. 115).This simple solution cannot be directly ported to languages typologically different fromEnglish, such as the morphology-rich, flexible word-order Polish. Morphology andword-order flexibility will be discussed shortly in the context of a proposed modificationof Espresso named Estratto.Pattern induction is based on the algorithm of Ravichandran and Hovy (2002),discussed earlier, with only one modification: all recognised multiword terms arereplaced with the label TR.The statistical reliability measures, introduced in Espresso for ranking patterns andinstances, follow the same basic scheme of recursive dependency of both measures:the reliability of instances depends on the patterns which extracted them and the otherway around. This scheme can be traced back to Snowball (Agichtein and Gravano,2000) and (Ravichandran and Hovy, 2002), but there it was implemented in a lesssophisticated form. Statistical evaluation is clearly this element which is missing whenworking with manually extracted patterns. Reliability calculation is Espresso’s keyelement, so we present it in more detail. For the needs of ranking and selectingpatterns, Espresso introduced a reliability measure:r π =∑i∈I ( pmi(i,p)max pmi|I|∗ r t (i))(4.1)p is a pattern, i – an instance, r t – a reliability measure for instances, pmi – thePointwise Mutual Information [PMI] measure, explained below, and |I| – the size ofthe instance set.The reliability of each seed delivered to Espresso is 1, so pattern reliability isproportional to the average strength of association between the pattern and the seeds