Paper accepted for the Journal of Natural Language Engineering

We have a new journal paper in the Natural Language Engineering journal summarizing the findings of the first part of our DFG JOIN-T (Joining Ontologies and semantics INduced from Text) project with the colleagues of the Language Technology Group of the University of Hamburg

Chris Biemann, Stefano Faralli, Alexander Panchenko and Simone Paolo Ponzetto: A framework for enriching lexical semantic resources with distributional semantics. To appear in the Journal of Natural Language Engineering. DOI: 10.1017/S135132491700047X. A pre-print version is available here

You can find the project homepage here.


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.