641 citations · 1k across the 33 of their papers we have counts for
9 papers · 2 filters
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…
Human and Machine Judgements for Russian Semantic Relatedness
Alexander Panchenko, Dmitry Ustalov, Nikolay Arefyev +4
Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments about semantic relatedness. On the other hand, th…
Fighting with the Sparsity of Synonymy Dictionaries
Dmitry Ustalov, Mikhail Chernoskutov, Chris Biemann +1
Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the st…
Making Sense of Word Embeddings
Maria Pelevina, Nikolay Arefyev, Chris Biemann +1
We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or…