100,946 research outputs found
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach for detecting semantic shifts between different viewpoints--broadly
defined as a set of texts that share a specific metadata feature, which can be
a time-period, but also a social entity such as a political party. For each
viewpoint, we learn a semantic space in which each word is represented as a low
dimensional neural embedded vector. The challenge is to compare the meaning of
a word in one space to its meaning in another space and measure the size of the
semantic shifts. We compare the effectiveness of a measure based on optimal
transformations between the two spaces with a measure based on the similarity
of the neighbors of the word in the respective spaces. Our experiments
demonstrate that the combination of these two performs best. We show that the
semantic shifts not only occur over time, but also along different viewpoints
in a short period of time. For evaluation, we demonstrate how this approach
captures meaningful semantic shifts and can help improve other tasks such as
the contrastive viewpoint summarization and ideology detection (measured as
classification accuracy) in political texts. We also show that the two laws of
semantic change which were empirically shown to hold for temporal shifts also
hold for shifts across viewpoints. These laws state that frequent words are
less likely to shift meaning while words with many senses are more likely to do
so.Comment: In Proceedings of the 26th ACM International on Conference on
Information and Knowledge Management (CIKM2017
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph
Representing knowledge as high-dimensional vectors in a continuous semantic
vector space can help overcome the brittleness and incompleteness of
traditional knowledge bases. We present a method for performing deductive
reasoning directly in such a vector space, combining analogy, association, and
deduction in a straightforward way at each step in a chain of reasoning,
drawing on knowledge from diverse sources and ontologies.Comment: AGI 201
A Connectionist Theory of Phenomenal Experience
When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as
many of them have been doing recently, there are two fundamentally distinct approaches available. Either
consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or
it is to be explained in terms of the computational processes defined over these vehicles. We call versions of
these two approaches vehicle and process theories of consciousness, respectively. However, while there may
be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because
of the influence exerted, on the one hand, by a large body of research which purports to show that the
explicit representation of information in the brain and conscious experience are dissociable, and on the
other, by the classical computational theory of mind – the theory that takes human cognition to be a species
of symbol manipulation. But two recent developments in cognitive science combine to suggest that a
reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the
experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer
reasonable to assume that the dissociability of conscious experience and explicit representation has been
adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in
cognitive science as it once was. It now has a lively competitor in the form of connectionism; and
connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory
of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It
takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit
representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some
common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Word embeddings have recently seen a strong increase in interest as a result
of strong performance gains on a variety of tasks. However, most of this
research also underlined the importance of benchmark datasets, and the
difficulty of constructing these for a variety of language-specific tasks.
Still, many of the datasets used in these tasks could prove to be fruitful
linguistic resources, allowing for unique observations into language use and
variability. In this paper we demonstrate the performance of multiple types of
embeddings, created with both count and prediction-based architectures on a
variety of corpora, in two language-specific tasks: relation evaluation, and
dialect identification. For the latter, we compare unsupervised methods with a
traditional, hand-crafted dictionary. With this research, we provide the
embeddings themselves, the relation evaluation task benchmark for use in
further research, and demonstrate how the benchmarked embeddings prove a useful
unsupervised linguistic resource, effectively used in a downstream task.Comment: in LREC 201
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