In this paper, we examine the distributional effects of tax evasion, using results from theoretical, experimental, empirical, and especially the general equilibrium literatures on tax evasion. Much – if not all – of this evidence concludes that the main beneficiaries of successful tax evasion are the tax evaders themselves, with distributional effects that largely favor higher income individual...

Distributional information is a potential cue for learning syntactic categories. Recent artificial grammar studies demonstrate sophisticated distributional learning by young infants. Here we investigate the possible mechanisms and representations underlying this ability. Does prosody constrain distributional analysis? What specific distributional relations do learners track? Twelve-month-old in...

Distributional models assume that the contexts of a linguistic unit (such as a word, a multi-word expression, a phrase, a sentence, etc.) provide information about the meaning of the linguistic unit (Firth, 1957; Harris, 1968). They have been widely applied in data-intensive lexical semantics (among other areas), and proven successful in diverse research issues, such as the representation and d...

This paper examines the effects of alternative distributional assumptions on aggregating across individuals with an identical utility function: so-called ”clones”. The standard representative agent paradigm is shown to be a special case, which is only satisfied under stringent distributional assumptions. Alternative, and more realistic distributional assumptions can produce vastly different est...

We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-sup...

This work explores computing distributional similarity between sub-parses, i.e., fragments of a parse tree, as an extension to general lexical distributional similarity techniques. In the same way that lexical distributional similarity is used to estimate lexical semantic similarity, we propose using distributional similarity between subparses to estimate the semantic similarity of phrases. Suc...

This paper describes and demonstrates a distributional semantic search service infrastructure for Linked Dataspaces. The center of the approach relies on the use of a distributional semantics infrastructure to provide semantic search and query services over data for users and applications, improving data accessibility over the Dataspace. By accessing the services through a REST API, users can s...

Using distributional analysis methods to compute semantic proximity links between words has become commonplace in NLP. This paper focuses on the issues of evaluating a distributional resource. We consider that setting up an evaluation procedure is a first step towards the characterization of the resource, and a way to improve its overall quality. We then propose a new protocol for in-text annot...