This paper investigates the role of Distributional Semantic Models (DSMs) into a Question Answering (QA) system. Our purpose is to exploit DSMs for answer re-ranking in QuestionCube, a framework for building QA systems. DSMs model words as points in a geometric space, also known as semantic space. Words are similar if they are close in that space. Our idea is that DSMs approaches can help to co...

We investigate how supervision (in the form of explicit instruction) interacts with distributional learning in the acquisition of the perception of a novel vowel contrast in a second language. An experiment with non-Dutch-speaking Bulgarians reveals that listeners who receive bimodal distributional training without explicit instruction can acquire new Dutch vowel contrasts, and that listeners w...

Givenness (Schwarzschild, 1999) is one of the central notions in the formal pragmatic literature discussing the organization of discourse. In this paper, we explore where distributional semantics can help address the gap between the linguistic insights into the formal pragmatic notion of Givenness and its implementation in computational

We introduce DISSECT, a toolkit to build and explore computational models of word, phrase and sentence meaning based on the principles of distributional semantics. The toolkit focuses in particular on compositional meaning, and implements a number of composition methods that have been proposed in the literature. Furthermore, DISSECT can be useful to researchers and practitioners who need models...

Abstract Isotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In nutshell, IDR learns that are calibrated, and simultaneously optimal relative to comprehensive classes relevant loss functions, subject isotonicity constraints in terms partial on covariate space. Non-parametric isotonic quantile b...

In this work we investigate commonalities and differences between the semantic representations of concrete and abstract words using human judgments and distributional semantics. We tackle the following questions: a) Does distributional similarity imply similarity in concreteness vs. abstractness? b) How do concrete and abstract context words co-occur with concrete and abstract words? c) Are our...

This paper introduces the wordspace package, which turns Gnu R into an interactive laboratory for research in distributional semantics. The package includes highly efficient implementations of a carefully chosen set of key functions, allowing it to scale up to real-life data sets.