An Iterative Approach to Estimating Frequencies over a Semantic Hierarchy

An Iterative Approach to Estimating Frequencies over a Semantic Hierarchy Stephen Clark and David Weir School of Cognitive and Computing Sciences University of Sussex Brighton, BN1 9HQ, UK {stephecl, davidw)@cogs, susx. ac. uk Abstract This paper is concerned with using a se- mantic hierarchy to estimate the frequency with which a word sense appears as a given argument of a verb, assuming the data is not sense disambiguated. The standard ap- proach is to split the count for any noun ap- pearing in the data equally among the al- ternative senses of the noun. This can lead to inaccurate estimates. We describe a re- estimation process which uses the accumu- lated counts of hypernyms of the alternative senses in order to redistribute the count. In order to choose a hypernym for each alter- native sense, we employ a novel technique which uses a X 2 test to measure the homo- geneity of sets of concepts in the hierarchy. 1 Introduction Knowledge of the constraints a verb places on the semantic types of its arguments (var- iously called selectional restrictions, selec- tional preferences, selectional constraints) is of use in many areas of natural language pro- cessing, particularly structural disambigua- tion. Recent treatments of selectional re- strictions have been probabilistic in nature (Resnik, 1993), (Li and Abe, 1998), (Ribas, 1995), (McCarthy, 1997), and estimation of the relevant probabilities has required corpus-based counts of the number of times word senses, or concepts, appear in the dif- ferent argument positions of verbs. A dif- ficulty arises due to the absence of a large volume of sense disambiguated data, as the counts have to be estimated from the nouns which appear in the corpus, most of which will have more than one sense. The tech- niques in Resnik (1993), Li and Abe (1998) and Ribas (1995) simply distribute the count equally among the alternative senses of a noun. Abney and Light (1998) have at- tempted to obtain selectional preferences us- ing the Expectation Maximization algorithm by encoding WordNet as a hidden Markov model and using a modified form of the forward-backward algorithm to estimate the parameters. The approach proposed in this paper is to use a re-estimation process which relies on counts being passed up a semantic hierar- chy, from the senses of nouns appearing in the data. We make use of the semantic hier- archy in WordNet (Fellbaum, 1998), which consists of word senses, or concepts, 1 related by the 'is-a' or 'is-a-kind-of' relation. If c' is a kind of c, then c is a hypernym of c', and c' a hyponym of c. Counts for any concept are transmitted up the hierarchy to all of the concept's hypernyms. Thus if eat chicken appears in the corpus, the count is transmit- ted up to <meat >, < :food>, and all the other hypernyms of that sense of chicken? The problem is how to distinguish the cor- rect sense of chicken in this case from incor- rect senses such as <wimp>. 3 We utilise the …

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