5 citations · 6 across the 4 of their papers we have counts for
6 papers
Large-scale Taxonomy Induction Using Entity and Word Embeddings
Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto +1
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal on…
Enriching Frame Representations with Distributionally Induced Senses
Stefano Faralli, Alexander Panchenko, Chris Biemann +1
We introduce a new lexical resource that enriches the Framester knowledge graph, which links Framnet, WordNet, VerbNet and other resources, with semantic features from text corpora…
A Framework for Enriching Lexical Semantic Resources with Distributional Semantics
Chris Biemann, Stefano Faralli, Alexander Panchenko +1
We present an approach to combining distributional semantic representations induced from text corpora with manually constructed lexical-semantic networks. While both kinds of seman…
Improving Hypernymy Extraction with Distributional Semantic Classes
Alexander Panchenko, Dmitry Ustalov, Stefano Faralli +2
In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using dis…
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
Alexander Panchenko, Eugen Ruppert, Stefano Faralli +2
We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occu…
Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
Alexander Panchenko, Fide Marten, Eugen Ruppert +4
Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-base…