A
Alexander Panchenko
Researcher at Skolkovo Institute of Science and Technology
Publications - 162
Citations - 1621
Alexander Panchenko is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Computer science & Semantic similarity. The author has an hindex of 19, co-authored 125 publications receiving 1202 citations. Previous affiliations of Alexander Panchenko include University of Hamburg & Bauman Moscow State Technical University.
Papers
More filters
Posted Content
Making Sense of Word Embeddings
TL;DR: This work presents a simple yet effective approach that can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words and an integrated WSD mechanism enables labeling of words in context with learned sense vectors.
Proceedings ArticleDOI
Making Sense of Word Embeddings
TL;DR: The authors induce a sense inventory from existing word embeddings via clustering of ego-networks of related words, which enables labeling of words in context with learned sense vectors, which gives rise to downstream applications.
Posted Content
Neural Entity Linking: A Survey of Models Based on Deep Learning
TL;DR: This work distills a generic architecture of a neural EL system and discusses its components, such as candidate generation, mention-context encoding, and entity ranking, summarizing prominent methods for each of them.
Proceedings ArticleDOI
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling
Alexander Panchenko,Stefano Faralli,Eugen Ruppert,Steffen Remus,Hubert Naets,Cédrick Fairon,Simone Paolo Ponzetto,Chris Biemann +7 more
TL;DR: This work presents a system for taxonomy construction that reached the first place in all subtasks of the SemEval 2016 challenge on Taxonomy Extraction Evaluation and shows that this method outperforms more complex and knowledge-rich approaches on most domains and languages.
Proceedings ArticleDOI
TARGER: Neural Argument Mining at Your Fingertips
Artem N. Chernodub,Oleksiy Oliynyk,Philipp Heidenreich,Alexander Bondarenko,Matthias Hagen,Chris Biemann,Alexander Panchenko +6 more
TL;DR: TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus is presented.