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Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva, Denis Teslenko, Artem Shelmanov +7
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed t…
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
Saba Anwar, Dmitry Ustalov, Nikolay Arefyev +3
We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsuperv…
Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
Dmitry Ustalov, Alexander Panchenko, Chris Biemann +1
We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of d…
Unsupervised Semantic Frame Induction using Triclustering
Dmitry Ustalov, Alexander Panchenko, Andrei Kutuzov +2
We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering p…
An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
Dmitry Ustalov, Denis Teslenko, Alexander Panchenko +3
In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with resp…
RUSSE: The First Workshop on Russian Semantic Similarity
Alexander Panchenko, Natalia Loukachevitch, Dmitry Ustalov +3
The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference. There exist a lot of comparativ…