Distributional semantics develops theories and methods to represent the meaning of natural
language expressions, with vectors encoding their statistical distribution in linguistic …

We present a vector space–based model for selectional preferences that predicts plausibility
scores for argument headwords. It does not require any lexical resources (such as …

This paper describes the application of so-called topic models to selectional preference
induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling …

In previous research in automatic verb classification, syntactic features have proved the most
useful features, although manual classifications rely heavily on semantic features. We show …

When it comes to inferring relations between entities in biomedical texts, Relation Extraction
(RE) has become key to biomedical information extraction. Although previous studies …

Using data from Reddy et al.(2011), we present a series of regression models of semantic
transparency in compound nouns. The results indicate that the frequencies of the compound …

We present an unsupervised method for inducing verb classes from verb uses in gigaword
corpora. Our method consists of two clustering steps: verb-specific semantic frames are first …

We describe a probabilistic framework for acquiring selectional preferences of linguistic
predicates and for using the acquired representations to model the effects of context on word …

Logical metonymy combines an event-selecting verb with an entity-denoting noun (eg, The
writer began the novel), triggering a covert event interpretation (eg, reading, writing) …

Subcategorization frames (SCFs), selectional preferences (SPs) and verb classes capture
related aspects of the predicateargument structure. We present the first unified framework for …