  We present an approach to combining distributional semantic representations
induced from text corpora with manually constructed lexical-semantic networks.
While both kinds of semantic resources are available with high lexical
coverage, our aligned resource combines the domain specificity and availability
of contextual information from distributional models with the conciseness and
high quality of manually crafted lexical networks. We start with a
distributional representation of induced senses of vocabulary terms, which are
accompanied with rich context information given by related lexical items. We
then automatically disambiguate such representations to obtain a full-fledged
proto-conceptualization, i.e. a typed graph of induced word senses. In a final
step, this proto-conceptualization is aligned to a lexical ontology, resulting
in a hybrid aligned resource. Moreover, unmapped induced senses are associated
with a semantic type in order to connect them to the core resource. Manual
evaluations against ground-truth judgments for different stages of our method
as well as an extrinsic evaluation on a knowledge-based Word Sense
Disambiguation benchmark all indicate the high quality of the new hybrid
resource. Additionally, we show the benefits of enriching top-down lexical
knowledge resources with bottom-up distributional information from text for
addressing high-end knowledge acquisition tasks such as cleaning hypernym
graphs and learning taxonomies from scratch.
