Learn More
This paper examines the extent to which verb diathesis alternations are empirically attested in corpus data. We automatically acquire alternating verbs from large balanced corpora by using partial-parsing methods and taxonomic information, and discuss how corpus data can be used to quantify linguistic generalizations. We estimate the productivity of an(More)
In this paper we investigate logical metonymy, i.e., constructions where the argument of a word in syntax appears to be different from that argument in logical form (e.g., enjoy the book means enjoy reading the book, and easy problem means a problem that is easy to solve). The systematic variation in the interpretation of such constructions suggests a rich(More)
This paper addresses the interpretation of nominalisations, a particular class of compound nouns whose head noun is derived from a verb and whose modifier is interpreted as an argument of this verb. Any attempt to automatically interpret nominalisations needs to take into account: (a) the selectional constraints imposed by the nominalised compound head, (b)(More)
In this paper we investigate polysemous adjectives whose meaning varies depending on the nouns they modify (e.g., fast). We acquire the meanings of these adjectives from a large corpus and propose a probabilistic model which provides a ranking on the set of possible interpretations. We identify lexical semantic information automatically by exploiting the(More)
Previous research has shown that the plausibility of an adjective-noun combination is correlated with its corpus co-occurrence frequency. In this paper, we estimate the co-occurrence frequencies of adjective-noun pairs that fail to occur in a 100 million word corpus using smoothing techniques and compare them to human plausibility ratings. Both class-based(More)
This paper provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilistic optimality-theoretic grammars proposed by Boersma and Hayes (2001). After a short introduction to the problem of grammar learning in OT, we discuss the limitations of the standard solution to this problem (the Constraint Demotion Algorithm by Tesar and(More)