Maria Lapata

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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 article addresses the interpretation of nominalizations, 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 nominalizations needs to take into account: (a) the selectional constraints imposed by the nominalized compound head,(More)
There is considerable evidence showing that the human sentence processor is guided by lexical preferences in resolving syntactic ambiguities. Several types of preferences have been identified, including morphological, syntactic, and semantic ones. However, the literature fails to provide a uniform account of what lexical preferences are and how they should(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 thesis deals with the acquisition and probabilistic modeling of lexical knowledge. A considerable body of work in lexical semantics concentrates on describing and representing systematic polysemy, i.e., the regular and predictable meaning alternations certain classes of words are subject to. Although the prevalence of the phenomenon has been long(More)
In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like(More)
The goal of the CLARITY project is to explore the use of discourse structure in the understanding of conversational speech. Within project CLARITY we aim to develop automatic classifiers for three levels of discourse structure in Spanish telephone conversations: speech acts, dialogue games, and discourse segments. This paper presents our first results and(More)