Over the last two decades, numerous algorithms have been developed that successfully capture something of the semantics of single words by looking at their distribution in text and comparing these distributions in a vector space model. However, it is not straightforward to construct meaning representations beyond the level of individual words – i.e. the combination of words into larger units – ...

Formal semantics has traditionally distinguished between extensional and intensional aspects of meaning, roughly equivalent to reference and what people intuitively call "meaning", respectively. It has primarily focused on extension, since this allows for predictions to be made and tested by building models of meaning that are directly grounded on reference, using set theoretic tools. Distribut...

Some words are more contentful than others: for instance, make is intuitively more general than produce and fifteen is more ‘precise’ than a group. In this paper, we propose to measure the ‘semantic content’ of lexical items, as modelled by distributional representations. We investigate the hypothesis that semantic content can be computed using the KullbackLeibler (KL) divergence, an informatio...

Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We...

Abstract We examine the computation of wide-angle corrections to galaxy power spectrum including redshift-space distortions and relativistic Doppler corrections, also multiple tracers with differing clustering, magnification evolution biases. show that inclusion contribution, as well radial derivative terms, are crucial for a consistent expansion large-scale surveys, both in single multi-tracer...

Maintainer Kimihiro Noguchi Depends R (>= 2.6.0), MASS License GPL (>= 2) Description The package ``nparLD'' is designed to perform nonparametric analysis of longitudinal data in factorial experiments. Longitudinal data are those which are collected from the same subjects over time, and they frequently arise in biological sciences. Non...

Inequality measurement involves explicit or implicit value judgements. The subjective approach to inequality measurement is a relatively new and fast-developing area which focueses direct attention on these judgements. It is "subjective" in the sense that it takes account of people's views on distributional comparisons. This paper surveys some of the principal contributions of recent years.

Compositional Distributional Semantics Models (CDSMs) are traditionally seen as an entire different world with respect to Tree Kernels (TKs). In this paper, we show that under a suitable regime these two approaches can be regarded as the same and, thus, structural information and distributional semantics can successfully cooperate in CSDMs for NLP tasks. Leveraging on distributed trees, we pres...

Formal and distributional semantic models offer complementary benefits in modeling meaning. The categorical compositional distributional model of meaning of Coecke et al. (2010) (abbreviated to DisCoCat in the title) combines aspects of both to provide a general framework in which meanings of words, obtained distributionally, are composed using methods from the logical setting to form sentence ...