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Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we(More)
Dominance and noncommutativity effects are investigated in relative clause descriptions of five conjunctive concepts (birds and pets, sports and games, vehicles and machines, office equipment and writing implements, and shoes and sports equipment). Both asymmetry phenomena are studied at the extensional level (using membership ratings) and at the(More)
This paper reports on the ways in which new entities are introduced into discourse. First, we present the evidence in support of a model of indefinite reference processing based on three principles: the listener's ability to make predictive inferences in order to decrease the unexpectedness of upcoming words, the availability to the speaker of grammatical(More)
In statistical NLP, Semantic Vector Spaces (SVS) are the standard technique for the automatic modeling of lexical semantics. However, it is largely unclear how these black-box techniques exactly capture word meaning. To explore the way an SVS structures the individual occurrences of words, we use a non-parametric MDS solution of a token-by-token similarity(More)
1 Computers and the Humanities xx: nnn-nnn, yyyy © yyyy. Kluwer Academic Publishers. Printed in the Netherlands. Abstract. In this text we present " profile-based linguistic uniformity " , a method designed to compare language varieties on the basis of a wide range of potentially heterogeneous linguistic variables. In many respects a parallel can be drawn(More)