41 citations · 152 across the 10 of their papers we have counts for
10 papers
BOS at LSCDiscovery: Lexical Substitution for Interpretable Lexical Semantic Change Detection
Artem Kudisov, Nikolay Arefyev
We propose a solution for the LSCDiscovery shared task on Lexical Semantic Change Detection in Spanish. Our approach is based on generating lexical substitutes that describe old an…
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution
Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy +1
Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a bac…
The Document Vectors Using Cosine Similarity Revisited
Zhang Bingyu, Nikolay Arefyev
The current state-of-the-art test accuracy (97.42\%) on the IMDB movie reviews dataset was reported by \citet{thongtan-phienthrakul-2019-sentiment} and achieved by the logistic reg…
Combining Neural Language Models for WordSense Induction
Nikolay Arefyev, Boris Sheludko, Tatiana Aleksashina
Word sense induction (WSI) is the problem of grouping occurrences of an ambiguous word according to the expressed sense of this word. Recently a new approach to this task was propo…
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
Nikolay Arefyev, Boris Sheludko, Alexander Podolskiy +1
Lexical substitution in context is an extremely powerful technology that can be used as a backbone of various NLP applications, such as word sense induction, lexical relation extra…
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…
How much does a word weigh? Weighting word embeddings for word sense induction
Nikolay Arefyev, Pavel Ermolaev, Alexander Panchenko
The paper describes our participation in the first shared task on word sense induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018). For each of…
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
Alexander Panchenko, Anastasiya Lopukhina, Dmitry Ustalov +4
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Roma…
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…
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
Dmitry Ustalov, Nikolay Arefyev, Chris Biemann +1
We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods requ…