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Inference in Probabilistic Logic Programs Using Lifted Explanations
In this paper, we consider the problem of lifted inference in the context of Prism-like probabilistic logic programming languages. Traditional inference in such languages involves the construction of an explanation graph for the query that treats each instance of a random variable separately. For many programs and queries, we observe that explanations can be summarized into substantially more compact structures introduced in this paper, called "lifted explanation graph". In contrast to existing lifted inference techniques, our method for constructing lifted explanations naturally generalizes existing methods for constructing explanation graphs. To compute probability of query answers, we solve recurrences generated from the lifted graphs. We show examples where the use of our technique reduces the asymptotic complexity of inference
On the universal structure of human lexical semantics
How universal is human conceptual structure? The way concepts are organized
in the human brain may reflect distinct features of cultural, historical, and
environmental background in addition to properties universal to human
cognition. Semantics, or meaning expressed through language, provides direct
access to the underlying conceptual structure, but meaning is notoriously
difficult to measure, let alone parameterize. Here we provide an empirical
measure of semantic proximity between concepts using cross-linguistic
dictionaries. Across languages carefully selected from a phylogenetically and
geographically stratified sample of genera, translations of words reveal cases
where a particular language uses a single polysemous word to express concepts
represented by distinct words in another. We use the frequency of polysemies
linking two concepts as a measure of their semantic proximity, and represent
the pattern of such linkages by a weighted network. This network is highly
uneven and fragmented: certain concepts are far more prone to polysemy than
others, and there emerge naturally interpretable clusters loosely connected to
each other. Statistical analysis shows such structural properties are
consistent across different language groups, largely independent of geography,
environment, and literacy. It is therefore possible to conclude the conceptual
structure connecting basic vocabulary studied is primarily due to universal
features of human cognition and language use.Comment: Press embargo in place until publicatio
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