jurafsky&martin_3rdEd_17 (1).pdf

P c 2240 the selectional association is thus a

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P ( c ) (22.40) The selectional association is thus a probabilistic measure of the strength of as- sociation between a predicate and a class dominating the argument to the predicate. Resnik estimates the probabilities for these associations by parsing a corpus, count- ing all the times each predicate occurs with each argument word, and assuming that each word is a partial observation of all the WordNet concepts containing the word. The following table from Resnik (1996) shows some sample high and low selectional associations for verbs and some WordNet semantic classes of their direct objects. Direct Object Direct Object Verb Semantic Class Assoc Semantic Class Assoc read WRITING 6.80 ACTIVITY -.20 write WRITING 7.26 COMMERCE 0 see ENTITY 5.79 METHOD -0.01 Selectional Preference via Conditional Probability An alternative to using selectional association between a verb and the WordNet class of its arguments, is to simply use the conditional probability of an argument word given a predicate verb. This simple model of selectional preferences can be used to directly model the strength of association of one verb (predicate) with one noun (argument). The conditional probability model can be computed by parsing a very large cor- pus (billions of words), and computing co-occurrence counts: how often a given verb occurs with a given noun in a given relation. The conditional probability of an argument noun given a verb for a particular relation P ( n | v , r ) can then be used as a selectional preference metric for that pair of words (Brockmann and Lapata, 2003) : P ( n | v , r ) = ( C ( n , v , r ) C ( v , r ) if C ( n , v , r ) > 0 0 otherwise The inverse probability P ( v | n , r ) was found to have better performance in some cases (Brockmann and Lapata, 2003) : P ( v | n , r ) = ( C ( n , v , r ) C ( n , r ) if C ( n , v , r ) > 0 0 otherwise In cases where it’s not possible to get large amounts of parsed data, another option, at least for direct objects, is to get the counts from simple part-of-speech based approximations. For example pairs can be extracted using the pattern ”V Det N”, where V is any form of the verb, Det is the—a— and N is the singular or plural form of the noun (Keller and Lapata, 2003) . An even simpler approach is to use the simple log co-occurrence frequency of the predicate with the argument log count ( v , n , r ) instead of conditional probability; this seems to do better for extracting preferences for syntactic subjects rather than objects (Brockmann and Lapata, 2003) .
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392 C HAPTER 22 S EMANTIC R OLE L ABELING Evaluating Selectional Preferences One way to evaluate models of selectional preferences is to use pseudowords ( Gale pseudowords et al. 1992c , Sch¨utze 1992a ). A pseudoword is an artificial word created by concate- nating a test word in some context (say banana ) with a confounder word (say door ) to create banana-door ). The task of the system is to identify which of the two words is the original word. To evaluate a selectional preference model (for example on the relationship between a verb and a direct object) we take a test corpus and select all verb tokens. For each verb token (say drive ) we select the direct object (e.g.,
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