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In this paper we explore the use of selectional preferences for detecting noncompositional verb-object combinations. To characterise the arguments in a given grammatical relationship we experiment with three models of selectional preference. Two use WordNet and one uses the entries from a distributional thesaurus as classes for representation. In previous work on selectional preference acquisition, the classes used for representation are selected according to the coverage of argument tokens rather than being selected according to the coverage of argument types. In our distributional thesaurus models and one of the methods using WordNet we select classes for representing the preferences by virtue of the number of argument types that they cover, and then only tokens under these classes which are representative of the argument head data are used to estimate the probability distribution for the selectional preference model. We demonstrate a highly significant correlation between measures which use these ‘typebased’ selectional preferences and compositionality judgements from a data set used in previous research. The type-based models perform better than the models which use tokens for selecting the classes. Furthermore, the models which use the automatically acquired thesaurus entries produced the best results. The correlation for the thesaurus models is stronger than any of the individual features used in previous research on the same dataset. 1

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Light verb constructions are a semi-productive class of multiword expression which have not yet been studied computationally in great detail. These constructions combine a restricted set of light verbs (verbs used with a subset of their full semantic features) with a large set of complements; this combination determines the predicate meaning of the expression. In this work we investigate the (semi-)productivity of light verb constructions which employ a predicative noun (a noun that has an argument structure) as their complement. We show that the productivity of these constructions depends on the semantic class of the complement. We develop three novel computational measures for quantifying the acceptability of candidate light verb constructions. Most of these measures meet or exceed the performance of an informed baseline, and reflect distinct trends in productivity along semantic classes and across light verbs. Good correlation and agreement with human judgments of construction acceptability are achieved.

A multiword predicate is the combination of a predicate (often a verb) with one or more of its arguments, that together form a single unit of predicative meaning. We focus on a broad class of multiword predicates, in which a verb combines with a noun in the direct object position (e.g., give a groan and shoot the breeze). The semantic interpretation of such multiword predicates involves a certain degree of idiosyncrasy; moreover, they are crosslinguistically frequent and appear in all text genres. Hence, they pose a great challenge to the current models of nat-ural language processing. Most existing computational models treat multiword predicates as syntactically-dependent word sequences or collocations. Such a treatment ignores other im-portant characteristics of these constructions, reflected in their distinct lexical and syntactic behaviour. Nonetheless, cues from the lexicosyntactic properties of multiword predicates have often been used in linguistic and psycholinguistic studies to explain their peculiar semantic behaviour. On the one hand, simple statistical approaches that only draw on the frequency of multiword predicates fail to account for much of the syntactic and semantic behaviour of these constructions. On the other hand, linguistic theories provide generalizations about the behaviour of multiword predicates that can be augmented with probabilistic knowledge about language in use. The main goal of the present study is to propose ways of combining the pre-dictive power of linguistic theories with the coverage and robustness of statistical techniques to acquire linguistically-plausible and reliable corpus-drawn knowledge about multiword predicates.

Abstract. We propose a method for automatically identifying individual instances of English verb-particle constructions (VPCs) in raw text. Our method employs the RASP parser and analysis of the sentential context of each VPC candidate to differentiate VPCs from simple combinations of a verb and prepositional phrase. We show that our proposed method has an F-score of 0.974 at VPC identification over the Brown Corpus and Wall Street Journal.

Abstract. We develop statistical measures for assessing the acceptability of a frequent class of multiword expressions. We also use the measures to estimate the degree of productivity of the expressions over semantically related nouns. We show that a linguistically-inspired measure outperforms a standard measure of collocation in its match with human judgments. The measure uses simple extraction techniques over non-marked-up web data. 1 Light Verb Constructions Recent work in NLP has recognized the challenges posed by the rich variety of multiword expressions (MWEs) (e.g., Sag et al., 2002). One unsolved problem posed by MWEs is how they should be encoded in a computational lexicon. Many MWEs are syntactically flexible; for these it is inappropriate to treat the full expression as a single word. However, fully compositional techniques can lead to overgeneralization, because flexible MWEs are often semi-productive: new expressions can only be formed from limited combinations of semantically and syntactically similar component words. In order to achieve accurate lexical acquisition methods, we must determine computational

corpus-based identification of light verb constructions

SemFrame generates FrameNet-like frames, complete with semantic roles and evoking lexical units. This output can enhance FrameNet by suggesting new frames, as well as additional lexical units that evoke existing frames. SemFrame output can also support the addition of frame semantic relationships to WordNet.

Abstract not found