36,685 research outputs found
Actual Causation in CP-logic
Given a causal model of some domain and a particular story that has taken
place in this domain, the problem of actual causation is deciding which of the
possible causes for some effect actually caused it. One of the most influential
approaches to this problem has been developed by Halpern and Pearl in the
context of structural models. In this paper, I argue that this is actually not
the best setting for studying this problem. As an alternative, I offer the
probabilistic logic programming language of CP-logic. Unlike structural models,
CP-logic incorporates the deviant/default distinction that is generally
considered an important aspect of actual causation, and it has an explicitly
dynamic semantics, which helps to formalize the stories that serve as input to
an actual causation problem
A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
In a recent article, Christiansen and Chater (2016) present a fundamental
constraint on language, i.e. a now-or-never bottleneck that arises from our
fleeting memory, and explore its implications, e.g., chunk-and-pass processing,
outlining a framework that promises to unify different areas of research. Here
we explore additional support for this constraint and suggest further
connections from quantitative linguistics and information theory
The sum of edge lengths in random linear arrangements
Spatial networks are networks where nodes are located in a space equipped
with a metric. Typically, the space is two-dimensional and until recently and
traditionally, the metric that was usually considered was the Euclidean
distance. In spatial networks, the cost of a link depends on the edge length,
i.e. the distance between the nodes that define the edge. Hypothesizing that
there is pressure to reduce the length of the edges of a network requires a
null model, e.g., a random layout of the vertices of the network. Here we
investigate the properties of the distribution of the sum of edge lengths in
random linear arrangement of vertices, that has many applications in different
fields. A random linear arrangement consists of an ordering of the elements of
the nodes of a network being all possible orderings equally likely. The
distance between two vertices is one plus the number of intermediate vertices
in the ordering. Compact formulae for the 1st and 2nd moments about zero as
well as the variance of the sum of edge lengths are obtained for arbitrary
graphs and trees. We also analyze the evolution of that variance in Erdos-Renyi
graphs and its scaling in uniformly random trees. Various developments and
applications for future research are suggested
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Reconstructing Native Language Typology from Foreign Language Usage
Linguists and psychologists have long been studying cross-linguistic
transfer, the influence of native language properties on linguistic performance
in a foreign language. In this work we provide empirical evidence for this
process in the form of a strong correlation between language similarities
derived from structural features in English as Second Language (ESL) texts and
equivalent similarities obtained from the typological features of the native
languages. We leverage this finding to recover native language typological
similarity structure directly from ESL text, and perform prediction of
typological features in an unsupervised fashion with respect to the target
languages. Our method achieves 72.2% accuracy on the typology prediction task,
a result that is highly competitive with equivalent methods that rely on
typological resources.Comment: CoNLL 201
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