A Wordnet from the Ground Up

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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
114 Chapter 4. Extracting Relation Instancesmeasured by PMI. Later, associating a pattern with a larger number of more reliableinstances increases the pattern’s reliability.The reliability of instances is defined symmetrically: replace the reliability ofinstances is with the reliability of patterns r π (i) and the set of instances by the set ofpatterns P :∑ ( )pmi(i,p)p∈P max pmi∗ r π (p)r π =(4.2)|P |PMI originates from Information Theory. It measures the strength of associationbetween two events:|x, p, y||∗, ∗, ∗|pmi(i, p) = log (4.3)|x, ∗, y||∗, p, ∗||x, p, y| is the number of occurrences of x and y in contexts matching the patternp, x, ∗, y – the number of co-occurrences of x and y in the corpus regardless of thepattern, and so on.The definition of pmi presented by Pantel and Pennacchiotti (2006) does not includethe constituent |∗, ∗, ∗| (the number of contexts). The PMI measure, however, shouldbe usually greater than 0, while pmi defined in (Pantel and Pennacchiotti, 2006) is not.The missing constituent is also suggested by the general definition of PMI:pmi(i, p) = log p(I, P )p(I)p(P )(4.4)Because PMI is significantly higher when instances and patterns are not numerous(e.g., < 10), PMI is multiplied by a discounting factor proposed in (Panteland Ravichandran, 2004) that decreases the bias towards infrequent events.In Espresso, generic patterns are defined as generating 10 times more instances thanpreviously accepted reliable patterns. They extract many instances but are characterisedby lower reliability. Generic patterns are not excluded by definition. They increaserecall (the number of correct instances extracted), but inevitably decrease precision.In order to prevent an excessive reduction of the precision, an additional measure ofconfidence of instances has been introduced. It is based on the evaluation of instancesagainst reliable patterns only and the additional data acquired by searching the Webwith the queries generated from instances and patterns:S(i) = ∑S p (i) ∗ r π(p)(4.5)Tp∈P RP R is the set of reliable patterns (given a threshold), S p (i, p) is the PMI between i andp measured on the data acquired from the Web (using Google queries) and T is thesum over the reliability of reliable patterns.
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A Wordnetfrom the Ground Up
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Work financed by the Polish Ministr
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6 Prefaceheartfelt thanks go to all
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8 Chapter 1. Motivation, Goals, Ear
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1.1. Motivation 11[a] special form
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1.1. Motivation 13Affect (Strappara
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1.2. The Goals of the plWordNet Pro
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1.2. The Goals of the plWordNet Pro
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1.3. Early Decisions 19Merge Model:
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1.3. Early Decisions 214. On the ot
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24 Chapter 2. Building a Wordnet Co
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48 Chapter 3. Discovering Semantic
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164 Chapter 4. Extracting Relation
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166 Chapter 5. Polish WordNet Today
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Appendix ATests for Lexico-semantic
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187Test for adjectives (T. IX)1. p1
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189RelatednessTest for nouns (T. XV
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BibliographyAgarwal, Abhaya and Alo
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Bibliography 193on Deep Lexical Acq
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Bibliography 195Derwojedowa, Magdal
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Bibliography 197Grefenstette, Grego
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Bibliography 199Kurc, Roman. (2008)
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Bibliography 201Mohammad, Saif and
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Bibliography 203. (2006) “The pot
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Bibliography 205and Technology 7(1-
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List of Tables2.1 The size of the c
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List of Figures2.1 The LU perspecti
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List of Figures 2114.16 Completely
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Index 213CBC, see Clustering by Com
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Index 215169, 177, 178, 180, 182hyp
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Index 217mutual hypernymy, 24Mutual
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Index 219SUMO, 14Supported Vector M
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A language without a wordnet is at
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A Wordnet from the Ground Up

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